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

Metabolomics in Prenatal Medicine: A Review

Pregnancy is a complicated and insidious state with various aspects to consider, including the well-being of the mother and child. Developing better non-invasive tests that cover a broader range of disorders with lower false-positive rates is a fundamental necessity in the prenatal medicine field, and, in this sense, the application of metabolomics could be extremely useful. Metabolomics measures and analyses the products of cellular biochemistry. As a biomarker discovery tool, the integrated holistic approach of metabolomics can yield new diagnostic or therapeutic approaches. In this review, we identify and summarize prenatal metabolomics studies and identify themes and controversies. We conducted a comprehensive search of PubMed and Google Scholar for all publications through January 2020 using combinations of the following keywords: nuclear magnetic resonance, mass spectrometry, metabolic profiling, prenatal diagnosis, pregnancy, chromosomal or aneuploidy, pre-eclampsia, fetal growth restriction, pre-term labor, and congenital defect. Metabolite detection with high throughput systems aided by advanced bioinformatics and network analysis allowed for the identification of new potential prenatal biomarkers and therapeutic targets. We took into consideration the scientific papers issued between the years 2000-2020, thus observing that the larger number of them were mainly published in the last 10 years. Initial small metabolomics studies in perinatology suggest that previously unidentified biochemical pathways and predictive biomarkers may be clinically useful. Although the scientific community is considering metabolomics with increasing attention for the study of prenatal medicine as well, more in-depth studies would be useful in order to advance toward the clinic world as the obtained results appear to be still preliminary. Employing metabolomics approaches to understand fetal and perinatal pathophysiology requires further research with larger sample sizes and rigorous testing of pilot studies using various omics and traditional hypothesis-driven experimental approaches.

First-trimester screening model for small-for-gestational-age using maternal clinical characteristics, serum screening markers, and placental volume: prospective cohort study

OBJECTIVE

To examine predictive value of first trimester placental volume, maternal clinical characteristics, and serum biomarkers in predicting small-for-gestational-age (SGA) singleton pregnancy.

METHODS

We conducted a prospective study to determine whether SGA is associated with maternal clinical factors. Between November 2016 to May 2018, 351 women were enrolled. We included pregnant women who underwent an integrated test for aneuploidy screening. Placental volume, maternal clinical characteristics, and maternal serum pregnancy-associated plasma protein A (PAPP-A) levels in the first trimester (at 10⁺⁰-13⁺⁶weeks) and maternal serum biomarkers after 15⁺⁰-22⁺⁶weeks were measured. We measured the width, height, and thickness of the placenta and calculated the placental volume using an established mathematical formula; then, we analyzed the association between SGA at delivery, estimated placental volume (EPV), maternal clinical characteristics, and maternal serum biomarkers by multiple logistic regression analysis.

RESULTS

In this study, 12.3% (43/351) neonates were delivered before 37 weeks of gestation, and the birth weight of 23.6% (83/351) was below the 10th percentile according to gestational age. On multivariate logistic regression, the MSAFP multiples of the median (MoM) showed the strongest association with SGA in singleton pregnancy (p < .01), and the PAPP-A MoM showed a weaker association in the multiple logistic regression than in the univariate regression (p = .0073 and .0068, respectively). Our prediction model using maternal age, maternal smoking, PAPP-A, and EPV achieved an area under the curve of 0.668 in singleton pregnancy.

CONCLUSION

During the first trimester, maternal clinical characteristics, serum biomarkers, and EPV may be used for predicting the risk of SGA in singleton pregnancy.

MicroRNA-206 predicts raised fetal growth retardation risk through the interaction with vascular endothelial growth factor in pregnancies

This study aimed to investigate the correlation of microRNA (miR)-206, vascular endothelial growth factor (VEGF) and miR-206/VEGF axis at different gestational ages with fetal growth retardation (FGR) risk in pregnancies.Eight hundred twenty pregnancies were consecutively recruited and their plasma samples were collected at early pregnancy (gestational age ≤ 13 weeks), middle pregnancy (gestational age: 14-27 weeks) and late pregnancy (gestational age ≥ 28 weeks), respectively. miR-206 expression and VEGF level in plasma were detected by quantitative polymerase chain reaction and enzyme-linked immunosorbent assay respectively. FGR was diagnosed based on the actual birth weight of fetus.miR-206 expression was negatively correlated with VEGF expression at early pregnancy, middle pregnancy and late pregnancy. Besides, miR-206 expression and miR-206/VEGF axis were elevated, but VEGF expression was decreased along with the increased gestational age. There were 74 FGR pregnancies and 746 non-FGR pregnancies. And both miR-206 expression and miR-206/VEGF axis were increased, but VEGF expression was reduced in FGR group compared to non-FGR group at early pregnancy, middle pregnancy and late pregnancy. Additionally, miR-206, VEGF and miR-206/VEGF axis at middle pregnancy and late pregnancy all showed good predictive values for FGR risk, and these indexes at late pregnancy exhibited the numerically highest predictive value for FGR risk. Furthermore, compared to miR-206 or VEGF alone, miR-206/VEGF axis presented with numerically higher predictive value for FGR risk.miR-206 predicts raised FGR risk through the interaction with VEGF in pregnancies, and it may serve as a novel biomarker for FGR prevention.

Metabolomics to reveal biomarkers and pathways of preterm birth: a systematic review and epidemiologic perspective

INTRODUCTION

Most known risk factors for preterm birth, a leading cause of infant morbidity and mortality, are not modifiable. Advanced molecular techniques are increasingly being applied to identify biomarkers and pathways important in disease development and progression.

OBJECTIVES

We review the state of the literature and assess it from an epidemiologic perspective.

METHODS

PubMed, Embase, CINAHL, and Cochrane Central were searched on January 31, 2019 for original articles published after 1998 that utilized an untargeted metabolomic approach to identify markers of preterm birth. Eligible manuscripts were peer-reviewed and included original data from untargeted metabolomics analyses of maternal tissue derived from human studies designed to determine mechanisms and predictors of preterm birth.

RESULTS

Of 2823 results, 14 articles met the inclusion requirements. There was little consistency in study design, outcome definition, type of biospecimen, or the inclusion of covariates and confounding factors, and few consistent associations with metabolites were identified in this review.

CONCLUSION

Studies to date on metabolomic predictors of preterm birth are highly heterogeneous in both methodology and resulting metabolite identification. There is an urgent need for larger studies in well-defined populations, to determine biomarkers predictive of preterm birth, and to reveal mechanisms and targets for development of intervention strategies.

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

INTRODUCTION

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

OBJECTIVE

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

DATA SOURCES

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

STUDY ELIGIBILITY CRITERIA

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

STUDY APPRAISAL AND SYNTHESIS METHODS

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

RESULTS

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

CONCLUSIONS AND IMPLICATIONS

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

PROSPERO REGISTRATION NUMBER

CRD42018089985.

Study on the Potential Biomarkers of Maternal Urine Metabolomics for Fetus with Congenital Heart Diseases Based on Modified Gas Chromatograph-Mass Spectrometer

Background

There has been significant research on the genetic and environmental factors of congenital heart defects (CHDs), but few causes of teratogenicity, especially teratogenic mechanisms, can be clearly identified. Metabolomics has a potential advantage in researching the relationship between external factors and CHD.

Objective

To find and identify the urinary potential biomarkers of pregnancy (including in the second and third trimesters) for fetuses with CHD based on modified gas chromatograph-mass spectrometer (GC-MS), which could reveal the possibility of high-risk factors for CHD and lay the foundation for early intervention, treatment, and prevention.

Methods

Using a case-control design, we measured the urinary potential biomarkers of maternal urine metabolomics based on GC-MS in a population-based sample of women whose infants were diagnosed with CHD (70 case subjects) or were healthy (70 control subjects). SIMCA-P 13.0 software, principal component analysis (PCA), orthogonal partial least squares-discriminant analysis (OPLS-DA), Wilcoxon-Mann-Whitney test, and logistics regression were used to find significant potential biomarkers.

Result

The 3D score graph of the OPLS-DA showed that the CHD and control groups were fully separated. The fitting parameters were R²x=0.78 and R²y=0.69, and the forecast rate was Q²=0.61, indicating a high forecast ability. According to the ranking of VIPs from the OPLS-DA models, we found 34 potential metabolic markers with a VIP > 1, and after two pairwise rank sum tests, we found 20 significant potential biomarkers, which were further used in multifactor logistic regressions. Significant substances, including 4-hydroxybenzeneacetic acid (OR=4.74, 95% CI: 1.06-21.06), 5-trimethylsilyloxy-n-valeric acid (OR=15.78, 95% CI: 2.33-106.67), propanedioic acid (OR=5.37, 95% CI: 1.87-15.45), hydracrylic acid (OR=6.23, 95% CI: 1.07-36.21), and uric acid (OR=5.23, 95% CI: 1.23-22.32), were associated with CHD.

Conclusion

The major potential biomarkers in maternal urine associated with CHD were 4-hydroxybenzeneacetic acid, 5-trimethylsilyloxy-n-valeric acid, propanedioic acid, hydracrylic acid, and uric acid, respectively. These results indicated that the short chain fatty acids (SCFAs) and aromatic amino acid metabolism may be relevant with CHD.