The Exploration of Fetal Growth Restriction Based on Metabolomics: A Systematic Review

Texture-Based Classification of Fetal Growth Restriction From Intrauterine Neurosonographic Image

OBJECTIVE

Fetal growth restriction (FGR) is a condition where fetuses fail to reach their genetic potential for growth, posing a significant health challenge for newborns. The aim of this research was to explore the efficacy of texture-based analysis of neurosonographic images in identifying FGR in fetuses, which may provide a promising tool for early assessment of FGR.

METHODS

A retrospective analysis collected 100 intrauterine neurosonographic images from 50 FGR and 50 gestational age-appropriate fetuses. Using MaZda software, approximately 300 texture features were extracted from occipital white matter (OWM) and cerebellum of intrauterine neurosonographic images, respectively. Then 10 optimal features were separately selected by 3 algorithms, including the Fisher coefficient method, the method of minimizing classification error probability and average correlation coefficients, and the mutual information coefficient method. Further, the 10 statistically most significant features were selected from these sets to form the mixed feature set. After nonlinear discriminant analysis was performed to reduce feature dimensionality, the artificial neural network (ANN) classifier was conducted, respectively.

RESULTS

For OWM and cerebellum, a total of 11 and 14 statistically significant features were selected. When the mixed feature sets of OWM and cerebellum were applied to ANN classifier, classification accuracy were 90.00% (κ = 0.800; P < .001) and 93.00% (κ = 0.860; P < .001), and the receiver operating characteristic curve for identifying FGR showed an area under the curve of 0.82 and 0.87.

CONCLUSIONS

Texture analysis of fetal intrauterine neurosonographic images is a feasible and noninvasive strategy for evaluating FGR fetuses.

Cord plasma metabolomic signatures of prenatal per- and polyfluoroalkyl substance (PFAS) exposures in the Boston Birth Cohort

BACKGROUND

Prenatal per- and polyfluoroalkyl substance (PFAS) exposures are associated with adverse offspring health outcomes, yet the underlying pathological mechanisms are unclear. Cord blood metabolomics can identify potentially important pathways associated with prenatal PFAS exposures, providing mechanistic insights that may help explain PFAS' long-term health effects.

METHODS

The study included 590 mother-infant dyads from the Boston Birth Cohort. We measured PFAS in maternal plasma samples collected 24-72 h after delivery and metabolites in cord plasma samples. We used metabolome-wide association studies and pathway enrichment analyses to identify metabolites and pathways associated with individual PFAS, and quantile-based g-computation models to examine associations of metabolites with the PFAS mixture. We used False Discovery Rate to account for multiple comparisons.

RESULTS

We found that 331 metabolites and 18 pathways were associated with ≥ 1 PFAS, and 38 metabolites were associated with the PFAS mixture, predominantly amino acids and lipids. Amino acids such as alanine and lysine and their pathways, crucial to energy generation, biosynthesis, and bone health, were associated with PFAS and may explain PFAS' effects on fetal growth restriction. Carnitines and carnitine shuttle pathway, associated with 7 PFAS and the PFAS mixture, are involved in mitochondrial fatty acid β-oxidation, which may predispose higher risks of fetal and child growth restriction and cardiovascular diseases. Lipids, such as glycerophospholipids and their related pathway, can contribute to insulin resistance and diabetes by modulating transporters on cell membranes, participating in β-cell signaling pathways, and inducing oxidative damage. Neurotransmission-related metabolites and pathways associated with PFAS, including cofactors, precursors, and neurotransmitters, may explain the PFAS' effects on child neurodevelopment. We observed stronger associations between prenatal PFAS exposures and metabolites in males.

CONCLUSIONS

This prospective birth cohort study contributes to the limited literature on potential metabolomic perturbations for prenatal PFAS exposures. Future studies are needed to replicate our findings and link prenatal PFAS associated metabolomic perturbations to long-term child health outcomes.

Liver transcriptomics-metabolomics integration reveals biological pathways associated with fetal programming in beef cattle

We investigated the long-term effects of prenatal nutrition on pre-slaughter Nelore bulls using integrative transcriptome and metabolome analyses of liver tissue. Three prenatal nutritional treatments were administered to 126 cows: NP (control, mineral supplementation only), PP (protein-energy supplementation in the third trimester), and FP (protein-energy supplementation throughout pregnancy). Liver samples from 22.5 ± 1-month-old bulls underwent RNA-Seq and targeted metabolomics. Weighted correlation network analysis (WGCNA) identified treatment-associated gene and metabolite co-expression modules, further analyzed using MetaboAnalyst 6.0 (metabolite over-representation analysis and transcriptome-metabolome integrative analysis) and Enrichr (gene over-representation analysis). We identified several significant gene and metabolite modules, as well as hub components associated with energy, protein and oxidative metabolism, regulatory mechanisms, epigenetics, and immune function. The NP transcriptome-metabolome analysis identified key pathways (aminoacyl t-RNA biosynthesis, gluconeogenesis, and PPAR signaling) and hub components (glutamic acid, SLC6A14). PP highlighted pathways (arginine and proline metabolism, TGF-beta signaling, glyoxylate and dicarboxylate metabolism) with arginine and ODC1 as hub components. This study highlights the significant impact of prenatal nutrition on the liver tissue of Nelore bulls, shedding light on critical metabolic pathways and hub components related to energy and protein metabolism, as well as immune system and epigenetics.

Metabolomic signatures associated with fetal growth restriction and small for gestational age: a systematic review

The pathways involved in the pathophysiology of fetal growth restriction (FGR) and small for gestational age (SGA) are incompletely understood. We conduct a systematic review to identify metabolomic signatures in maternal and newborn tissues and body fluids samples associated with FGR/SGA. Here, we report that 825 non-duplicated metabolites were significantly altered across the 48 included studies using 10 different human biological samples, of which only 56 (17 amino acids, 12 acylcarnitines, 11 glycerophosphocholines, six fatty acids, two hydroxy acids, and eight other metabolites) were significantly and consistently up- or down-regulated in more than one study. Three amino acid metabolism-related pathways and one related with lipid metabolism are significantly associated with FGR and/or SGA: biosynthesis of unsaturated fatty acids in umbilical cord blood, and phenylalanine, tyrosine and tryptophan biosynthesis, valine, leucine and isoleucine biosynthesis, and phenylalanine metabolism in newborn dried blood spot. Significantly enriched metabolic pathways were not identified in the remaining biological samples. Whether these metabolites are in the causal pathways or are biomarkers of fetal nutritional deficiency needs to be explored in large, well-phenotyped cohorts.

Dysregulated arginine metabolism in precursor B-cell acute lymphoblastic leukemia in children: a metabolomic study

BACKGROUND

Precursor B-cell acute lymphoblastic leukemia (B-ALL) is the most common cancers in children. Failure of induction chemotherapy is a major factor leading to relapse and death in children with B-ALL. Given the importance of altered metabolites in the carcinogenesis of pediatric B-ALL, studying the metabolic profile of children with B-ALL during induction chemotherapy and in different minimal residual disease (MRD) status may contribute to the management of pediatric B-ALL.

METHODS

We collected paired peripheral blood plasma samples from children with B-ALL at pre- and post-induction chemotherapy and analyzed the metabolomic profiling of these samples by ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS). Healthy children were included as controls. We selected metabolites that were depleted in pediatric B-ALL and analyzed the concentrations in pediatric B-ALL samples. In vitro, we study the effects of the selected metabolites on the viability of ALL cell lines and the sensitivity to conventional chemotherapeutic agents in ALL cell lines.

RESULTS

Forty-four metabolites were identified with different levels between groups. KEGG pathway enrichment analyses revealed that dysregulated linoleic acid (LA) metabolism and arginine (Arg) biosynthesis were closely associated with pediatric B-ALL. We confirmed that LA and Arg were decreased in pediatric B-ALL samples. The treatment of LA and Arg inhibited the viability of NALM-6 and RS4;11 cells in a dose-dependent manner, respectively. Moreover, Arg increased the sensitivity of B-ALL cells to L-asparaginase and daunorubicin.

CONCLUSION

Arginine increases the sensitivity of B-ALL cells to the conventional chemotherapeutic drugs L-asparaginase and daunorubicin. This may represent a promising therapeutic approach.

Impact of Maternal Pre-Pregnancy Underweight on Cord Blood Metabolome: An Analysis of the Population-Based Survey of Neonates in Pomerania (SNiP)

Intrauterine growth restriction leads to an altered lipid and amino acid profile in the cord blood at the end of pregnancy. Pre-pregnancy underweight is an early risk factor for impaired fetal growth. The aim of this study was to investigate whether a pre-pregnancy body mass index (ppBMI) of <18.5 kg/m2, as early as at the beginning of pregnancy, is associated with changes in the umbilical cord metabolome. In a sample of the Survey of Neonates in Pomerania (SNIP) birth cohort, the cord blood metabolome of n = 240 newborns of mothers with a ppBMI of <18.5 kg/m2 with n = 208 controls (ppBMI of 18.5-24.9 kg/m2) was measured by NMR spectrometry. A maternal ppBMI of <18.5 kg/m2 was associated with increased concentrations of HDL4 cholesterol, HDL4 phospholipids, VLDL5 cholesterol, HDL 2, and HDL4 Apo-A1, as well as decreased VLDL triglycerides and HDL2 free cholesterol. A ppBMI of <18.5 kg/m2 combined with poor intrauterine growth (a gestational weight gain (GWG) < 25th percentile) was associated with decreased concentrations of total cholesterol; cholesterol transporting lipoproteins (LDL4, LDL6, LDL free cholesterol, and HDL2 free cholesterol); LDL4 Apo-B; total Apo-A2; and HDL3 Apo-A2. In conclusion, maternal underweight at the beginning of pregnancy already results in metabolic changes in the lipid profile in the cord blood, but the pattern changes when poor GWG is followed by pre-pregnancy underweight.

Prediction of spontaneous preterm birth using supervised machine learning on metabolomic data: A case-cohort study

OBJECTIVES

To identify and internally validate metabolites predictive of spontaneous preterm birth (sPTB) using multiple machine learning methods and sequential maternal serum samples, and to predict spontaneous early term birth (sETB) using these metabolites.

DESIGN

Case-cohort design within a prospective cohort study.

SETTING

Cambridge, UK.

POPULATION OR SAMPLE

A total of 399 Pregnancy Outcome Prediction study participants, including 98 cases of sPTB.

METHODS

An untargeted metabolomic analysis of maternal serum samples at 12, 20, 28 and 36 weeks of gestation was performed. We applied six supervised machine learning methods and a weighted Cox model to measurements at 28 weeks of gestation and sPTB, followed by feature selection. We used logistic regression with elastic net penalty, followed by best subset selection, to reduce the number of predictive metabolites further. We applied coefficients from the chosen models to measurements from different gestational ages to predict sPTB and sETB.

MAIN OUTCOME MEASURES

sPTB and sETB.

RESULTS

We identified 47 metabolites, mostly lipids, as important predictors of sPTB by two or more methods and 22 were identified by three or more methods. The best 4-predictor model had an optimism-corrected area under the receiver operating characteristics curve (AUC) of 0.703 at 28 weeks of gestation. The model also predicted sPTB in 12-week samples (0.606, 95% CI 0.544-0.667) and 20-week samples (0.657, 95% CI 0.597-0.717) and it predicted sETB in 36-week samples (0.727, 95% CI 0.606-0.849). A lysolipid, 1-palmitoleoyl-GPE (16:1)*, was the strongest predictor of sPTB at 12 weeks of gestation (0.609, 95% CI 0.548-0.670), 20 weeks (0.630, 95% CI 0.569-0.690) and 28 weeks (0.660, 95% CI 0.599-0.722), and of sETB at 36 weeks (0.739, 95% CI 0.618-0.860).

CONCLUSIONS

We identified and internally validated maternal serum metabolites predictive of sPTB. A lysolipid, 1-palmitoleoyl-GPE (16:1)*, is a novel predictor of sPTB and sETB. Further validation in external populations is required.

Potential Association of Gut Microbial Metabolism and Circulating mRNA Based on Multiomics Sequencing Analysis in Fetal Growth Restriction

Objective

Fetal growth restriction (FGR) is a significant contributor to negative pregnancy and postnatal developmental outcomes. Currently, the exact pathological mechanism of FGR remains unknown. This study aims to utilize multiomics sequencing technology to investigate potential relationships among mRNA, gut microbiota, and metabolism in order to establish a theoretical foundation for diagnosing and understanding the molecular mechanisms underlying FGR.

Methods

In this study, 11 healthy pregnant women and nine pregnant women with FGR were divided into Control group and FGR group based on the health status. Umbilical cord blood, maternal serum, feces, and placental tissue samples were collected during delivery. RNA sequencing, 16S rRNA sequencing, and metabolomics methods were applied to analyze changes in umbilical cord blood circulating mRNA, fecal microbiota, and metabolites. RT-qPCR, ELISA, or western blot were used to detect the expression of top 5 differential circulating mRNA in neonatal cord blood, maternal serum, or placental tissue samples. Correlation between differential circulating mRNA, microbiota, and metabolites was analyzed by the Spearman coefficient.

Results

The top 5 mRNA genes in FGR were altered with the downregulation of TRIM34, DEFA3, DEFA1B, DEFA1, and QPC, and the upregulation of CHPT1, SMOX, FAM83A, GDF15, and NAPG in newborn umbilical cord blood, maternal serum, and placental tissue. The abundance of Bacteroides, Akkermansia, Eubacterium_coprostanoligenes_group, Phascolarctobacterium, Parasutterella, Odoribacter, Lachnospiraceae_UCG_010, and Dielma were significantly enriched in the FGR group. Metabolites such as aspartic acid, methionine, alanine, L-tryptophan, 3-methyl-2-oxovalerate, and ketoleucine showed notable functional alterations. Spearman correlation analysis indicated that metabolites like methionine and alanine, microbiota (Tyzzerella), and circulating mRNA (TRIM34, SMOX, FAM83A, NAPG) might play a role as mediators in the communication between the gut and circulatory system interaction in FGR.

Conclusion

Metabolites (METHIONINE, alanine) as well as microbiota (Tyzzerella) and circulating mRNA (TRIM34, SMOX, FAM83A, NAPG) were possible mediators that communicated the interaction between the gut and circulatory systems in FGR.

Predicting the onset of preeclampsia by longitudinal monitoring of metabolic changes throughout pregnancy with Raman spectroscopy

Preeclampsia is a life-threatening pregnancy disorder. Current clinical assays cannot predict the onset of preeclampsia until the late 2nd trimester, which often leads to poor maternal and neonatal outcomes. Here we show that Raman spectroscopy combined with machine learning in pregnant patient plasma enables rapid, highly sensitive maternal metabolome screening that predicts preeclampsia as early as the 1st trimester with >82% accuracy. We identified 12, 15 and 17 statistically significant metabolites in the 1st, 2nd and 3rd trimesters, respectively. Metabolic pathway analysis shows multiple pathways corresponding to amino acids, fatty acids, retinol, and sugars are enriched in the preeclamptic cohort relative to a healthy pregnancy. Leveraging Pearson's correlation analysis, we show for the first time with Raman Spectroscopy that metabolites are associated with several clinical factors, including patients' body mass index, gestational age at delivery, history of preeclampsia, and severity of preeclampsia. We also show that protein quantification alone of proinflammatory cytokines and clinically relevant angiogenic markers are inadequate in identifying at-risk patients. Our findings demonstrate that Raman spectroscopy is a powerful tool that may complement current clinical assays in early diagnosis and in the prognosis of the severity of preeclampsia to ultimately enable comprehensive prenatal care for all patients.

Metabolic dynamics and prediction of sFGR and adverse fetal outcomes: a prospective longitudinal cohort study

BACKGROUND

Selective fetal growth restriction (sFGR) is an extreme complication that significantly increases the risk of perinatal mortality and long-term adverse neurological outcomes in offspring, affecting approximately 15% of monochorionic diamniotic (MCDA) twin pregnancies. The lack of longitudinal cohort studies hinders the early prediction and intervention of sFGR.

METHODS

We constructed a prospective longitudinal cohort study of sFGR, and quantified 25 key metabolites in 337 samples from maternal plasma in the first, second, and third trimester and from cord plasma. In particular, our study examined fetal growth and brain injury data from ultrasonography and used the Ages and Stages Questionnaire-third edition subscale (ASQ-3) to evaluate the long-term neurocognitive behavioral development of infants aged 2-3 years. Furthermore, we correlated metabolite levels with ultrasound data, including physical development and brain injury indicators, and ASQ-3 data using Spearman's-based correlation tests. In addition, special combinations of differential metabolites were used to construct predictive models for the occurrence of sFGR and fetal brain injury.

RESULTS

Our findings revealed various dynamic patterns for these metabolites during pregnancy and a maximum of differential metabolites between sFGR and MCDA in the second trimester (n = 8). The combination of L-phenylalanine, L-leucine, and L-isoleucine in the second trimester, which were closely related to fetal growth indicators, was highly predictive of sFGR occurrence (area under the curve [AUC]: 0.878). The combination of L-serine, L-histidine, and L-arginine in the first trimester and creatinine in the second trimester was correlated with long-term neurocognitive behavioral development and showed the capacity to identify fetal brain injury with high accuracy (AUC: 0.94).

CONCLUSIONS

The performance of maternal plasma metabolites from the first and second trimester is superior to those from the third trimester and cord plasma in discerning sFGR and fetal brain injury. These metabolites may serve as useful biomarkers for early prediction and promising targets for early intervention in clinical settings.

Cardiovascular prevention in childhood: a consensus document of the Italian Society of Cardiology Working Group on Congenital Heart Disease and Cardiovascular Prevention in Paediatric Age

Cardiovascular diseases (CVD) may be manifested from a very early age. Genetic and environmental (epigenetic) factors interact to affect development and give rise to an abnormal phenotypical expression of genetic information, although not eliciting changes in the nucleotide sequence of DNA. It has been scientifically proven that increased oxidative stress (OS) caused by disease (overweight, obesity, diabetes), nutritional imbalances, unhealthy lifestyles (smoking, alcohol, substance abuse) in the mother during pregnancy may induce placental dysfunction, intrauterine growth restriction, prematurity, low birth weight, postnatal adiposity rebound, metabolic alterations and consequent onset of traditional cardiovascular risk factors. OS represents the cornerstone in the onset of atherosclerosis and manifestation of CVD following an extended asymptomatic period. OS activates platelets and monocytes eliciting the release of pro-inflammatory, pro-atherogenic and pro-oxidising substances resulting in endothelial dysfunction, decrease in flow-mediated arterial dilatation and increase in carotid intima-media thickness. The prevention of CVD is defined as primordial (aimed at preventing risk factors development), primary (aimed at early identification and treatment of risk factors), secondary (aimed at reducing risk of future events in patients who have already manifested a cardiovascular event), and tertiary (aimed at limiting the complex outcome of disease). Atherosclerosis prevention should be implemented as early as possible. Appropriate screening should be carried out to identify children at high risk who are apparently healthy and implement measures including dietary and lifestyle changes, addition of nutritional supplements and, lastly, pharmacological treatment if risk profiles fail to normalise. Reinstating endothelial function during the reversible stage of atherosclerosis is crucial.