Non-targeted urinary metabolomics in pregnancy and associations with fetal growth restriction

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.

Association of cord plasma metabolites with birth weight: results from metabolomic and lipidomic studies of discovery and validation cohorts

OBJECTIVE

Birth weight is a good predictor of fetal intrauterine growth and long-term health, and several studies have evaluated the relationship between metabolites and birth weight. The aim of this study was to investigate the association of cord blood metabolomics and lipidomics with birth weight, using a two-stage discovery and validation approach.

METHODS

Firstly, a pseudotargeted metabolomics approach was applied to detect metabolites in 504 cord blood samples in the discovery set enrolled from the Wuhan Healthy Baby Cohort, China. Metabolome-wide association scan analysis and pathway enrichment were applied to identify metabolites and metabolic pathways that were significantly associated with birth weight adjusted for gestational age Z-score (BW Z-score). Logistic regression models were used to analyze the association of metabolites in the most significantly associated pathways with small-for-gestational age (SGA) at delivery and low birth weight (LBW). Subsequently, 350 cord blood samples in a validation cohort were subjected to targeted analysis to validate the metabolites identified by screening in the discovery cohort.

RESULTS

In the discovery set, of 2566 metabolites detected, 2418 metabolites were retained for subsequent analysis after data preprocessing. Of these, 513 metabolites were significantly associated with BW Z-score (P-value adjusted for false discovery rate (PFDR) < 0.05), of which 298 Kyoto Encyclopedia of Genes and Genomes (KEGG)-annotated metabolites were included in the pathway analysis. The primary bile acid biosynthesis pathway was the most relevant metabolic pathway associated with BW Z-score. Elevated cord plasma primary bile acids were associated with lower BW Z-score and higher risk of SGA or LBW in the discovery and validation cohorts. In the validation set, a 2-fold increase in taurochenodeoxycholic acid (TCDCA) and in taurocholic acid (TCA) was associated with a decrease in BW Z-score (estimated β coefficient, -0.10 (95% CI, -0.20 to 0.00) and -0.18 (95% CI, -0.31 to -0.04), respectively), after adjusting for covariates. In addition, a 2-fold increase in cord plasma TCDCA and of cord plasma TCA was associated with an increased risk of SGA (adjusted odds ratio (OR), 1.52 (95% CI, 1.00-2.30) and 1.77 (95% CI, 1.05-2.98), respectively). The adjusted OR for LBW, for a 2-fold increase in TCDCA and TCA concentration, were 2.39 (95% CI, 1.00-5.71) and 3.21 (95% CI, 0.96-10.74), respectively.

CONCLUSIONS

These results indicate a significant association of elevated primary bile acids, particularly TCDCA and TCA, in cord blood with lower BW Z-score, as well as increased risk of SGA and LBW. Abnormalities of primary bile acid metabolism may play an important role in restricted fetal development. © 2024 International Society of Ultrasound in Obstetrics and Gynecology.

Opportunities and challenges for sample preparation and enrichment in mass spectrometry for single-cell metabolomics

Single-cell heterogeneity in metabolism, drug resistance and disease type poses the need for analytical techniques for single-cell analysis. As the metabolome provides the closest view of the status quo in the cell, studying the metabolome at single-cell resolution may unravel said heterogeneity. A challenge in single-cell metabolome analysis is that metabolites cannot be amplified, so one needs to deal with picolitre volumes and a wide range of analyte concentrations. Due to high sensitivity and resolution, MS is preferred in single-cell metabolomics. Large numbers of cells need to be analysed for proper statistics; this requires high-throughput analysis, and hence automation of the analytical workflow. Significant advances in (micro)sampling methods, CE and ion mobility spectrometry have been made, some of which have been applied in high-throughput analyses. Microfluidics has enabled an automation of cell picking and metabolite extraction; image recognition has enabled automated cell identification. Many techniques have been used for data analysis, varying from conventional techniques to novel combinations of advanced chemometric approaches. Steps have been set in making data more findable, accessible, interoperable and reusable, but significant opportunities for improvement remain. Herein, advances in single-cell analysis workflows and data analysis are discussed, and recommendations are made based on the experimental goal.

Similarity network fusion to identify phenotypes of small-for-gestational-age fetuses

Fetal growth restriction (FGR) affects 5-10% of pregnancies, is the largest contributor to fetal death, and can have long-term consequences for the child. Implementation of a standard clinical classification system is hampered by the multiphenotypic spectrum of small fetuses with substantial differences in perinatal risks. Machine learning and multiomics data can potentially revolutionize clinical decision-making in FGR by identifying new phenotypes. Herein, we describe a cluster analysis of FGR based on an unbiased machine-learning method. Our results confirm the existence of two subtypes of human FGR with distinct molecular and clinical features based on multiomic analysis. In addition, we demonstrated that clusters generated by machine learning significantly outperform single data subtype analysis and biologically support the current clinical classification in predicting adverse maternal and neonatal outcomes. Our approach can aid in the refinement of clinical classification systems for FGR supported by molecular and clinical signatures.

A screening test proposal for congenital defects based on maternal serum metabolomics profile

BACKGROUND

Historically, noninvasive techniques are only able to identify chromosomal anomalies that accounted for <50% of all congenital defects; the other congenital defects are diagnosed via ultrasound evaluations in the later stages of pregnancy. Metabolomic analysis may provide an important improvement, potentially addressing the need for novel noninvasive and multicomprehensive early prenatal screening tools. A growing body of evidence outlines notable metabolic alterations in different biofluids derived from pregnant women carrying fetuses with malformations, suggesting that such an approach may allow the discovery of biomarkers common to most fetal malformations. In addition, metabolomic investigations are inexpensive, fast, and risk-free and often generate high performance screening tests that may allow early detection of a given pathology.

OBJECTIVE

This study aimed to evaluate the diagnostic accuracy of an ensemble machine learning model based on maternal serum metabolomic signatures for detecting fetal malformations, including both chromosomal anomalies and structural defects.

STUDY DESIGN

This was a multicenter observational retrospective study that included 2 different arms. In the first arm, a total of 654 Italian pregnant women (334 cases with fetuses with malformations and 320 controls with normal developing fetuses) were enrolled and used to train an ensemble machine learning classification model based on serum metabolomics profiles. In the second arm, serum samples obtained from 1935 participants of the New Zealand Screening for Pregnancy Endpoints study were blindly analyzed and used as a validation cohort. Untargeted metabolomics analysis was performed via gas chromatography-mass spectrometry. Of note, 9 individual machine learning classification models were built and optimized via cross-validation (partial least squares-discriminant analysis, linear discriminant analysis, naïve Bayes, decision tree, random forest, k-nearest neighbor, artificial neural network, support vector machine, and logistic regression). An ensemble of the models was developed according to a voting scheme statistically weighted by the cross-validation accuracy and classification confidence of the individual models. This ensemble machine learning system was used to screen the validation cohort.

RESULTS

Significant metabolic differences were detected in women carrying fetuses with malformations, who exhibited lower amounts of palmitic, myristic, and stearic acids; N-α-acetyllysine; glucose; L-acetylcarnitine; fructose; para-cresol; and xylose and higher levels of serine, alanine, urea, progesterone, and valine (P<.05), compared with controls. When applied to the validation cohort, the screening test showed a 99.4%±0.6% accuracy (specificity of 99.9%±0.1% [1892 of 1894 controls correctly identified] with a sensitivity of 78%±6% [32 of 41 fetal malformations correctly identified]).

CONCLUSION

This study provided clinical validation of a metabolomics-based prenatal screening test to detect the presence of congenital defects. Further investigations are needed to enable the identification of the type of malformation and to confirm these findings on even larger study populations.

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

BACKGROUND

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

OBJECTIVE

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

STUDY DESIGN

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

RESULTS

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

CONCLUSION

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

Dapagliflozin Presented Nonalcoholic Fatty Liver Through Metabolite Extraction and AMPK/NLRP3 Signaling Pathway

In recent years, the incidence rate of nonalcoholic fatty liver disease (NAFLD) has been increasing year by year. The experiments conducted on rat elucidated the effect and underlying mechanism of dapagliflozin in NAFLD. Sprague Dawley rats were fed with HFD (Fat accounts for 52%, carbohydrate 34% and protein 14%) for 12 weeks as NAFLD model. Dapagliflozin presented NAFLD in rat model. Dapagliflozin reduced oxidative stress and inflammation in rat model of NAFLD. Dapagliflozin reduced oxidative stress and inflammation in vitro model of NAFLD. Dapagliflozin in a model of NAFLD metabolized into histamine H1 receptor, caffeine metabolism, mannose type O-glycan biosynthesis, choline metabolism in cancer, tryptophan metabolism, and glycerophospholipid metabolism. Dapagliflozin induced AMPK/NLRP3 signaling pathway. The regulation of AMPK/NLRP3 signaling pathway affected the effects of dapagliflozin on nonalcoholic fatty liver. In summary, dapagliflozin plays a preventative role in NAFLD through metabolite extraction, the inhibition of oxidative stress, and inflammation by AMPK/NLRP3 signaling pathway. Dapagliflozin may be a potential therapeutic agent for oxidative stress and inflammation in model of NAFLD.

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

Fetal growth restriction (FGR) is a common complication of pregnancy and a significant cause of neonatal morbidity and mortality. The adverse effects of FGR can last throughout the entire lifespan and increase the risks of various diseases in adulthood. However, the etiology and pathogenesis of FGR remain unclear. This study comprehensively reviewed metabolomics studies related with FGR in pregnancy to identify potential metabolic biomarkers and pathways. Relevant articles were searched through two online databases (PubMed and Web of Science) from January 2000 to July 2022. The reported metabolites were systematically compared. Pathway analysis was conducted through the online MetaboAnalyst 5.0 software. For humans, a total of 10 neonatal and 14 maternal studies were included in this review. Several amino acids, such as alanine, valine, and isoleucine, were high frequency metabolites in both neonatal and maternal studies. Meanwhile, several pathways were suggested to be involved in the development of FGR, such as arginine biosynthesis, arginine, and proline metabolism, glyoxylate and dicarboxylate metabolism, and alanine, aspartate, and glutamate metabolism. In addition, we also included 8 animal model studies, in which three frequently reported metabolites (glutamine, phenylalanine, and proline) were also present in human studies. In general, this study summarized several metabolites and metabolic pathways which may help us to better understand the underlying metabolic mechanisms of FGR.

Diet and human reproductive system: Insight of omics approaches

Nutrition and lifestyle have a great impact on reproduction and infertility in humans, as they are essential for certain processes such as implantation, placental growth, angiogenesis, and the transfer of nutrients from the mother to the fetus. The aim of this review is to provide the interconnection between nutrition and reproductive health through the insight of omics approaches (including metabolomics and nutrigenomics). The effect of various macronutrients, micronutrients, and some food‐associated components on male and female reproduction was discussed. Recent research work was collected through database search from 2010 to 2020 to identify eligible studies. Alterations of metabolic pathways in pregnant women were deliberated with an emphasis on different strategies of lifestyle and dietary interventions. Several nutritional methods, which are important for embryonic and child neurological development, nutritional supplements to lactation, and improved gestational length along with birth weight have been emphasized. Considerable advances in omics strategies show potential technological development for improving human reproductive health.

Associations of maternal and infant metabolite profiles with foetal growth and the odds of adverse birth outcomes

BACKGROUND

Adaptations in maternal and foetal metabolic pathways may predispose to altered foetal growth and adverse birth outcomes.

OBJECTIVE

To assess the associations of maternal early-pregnancy metabolite profiles and infant metabolite profiles at birth with foetal growth from first trimester onwards and the odds of adverse birth outcomes.

METHODS

In a prospective population-based cohort among 976 Dutch pregnant women and their children, serum concentrations of amino acids, non-esterified fatty acids (NEFA), phospholipids (PL) and carnitines in maternal early-pregnancy blood and in cord blood were obtained by liquid-chromatography tandem mass spectrometry. Information on foetal growth was available from first trimester onwards.

RESULTS

After false discovery rate correction for multiple testing, higher infant total and individual NEFA concentrations were associated with a lower weight, length, and head circumference at birth. Higher infant total and individual acyl-lysophosphatidylcholine (lyso.PC.a) and alkyl-lysophosphatidylcholine concentrations were associated with higher weight and head circumference (lyso.PC.a only) at birth, higher odds of LGA and lower odds of SGA. Few individual maternal metabolites were associated with foetal growth measures in third trimester and at birth, but not with the odds of adverse birth outcomes.

CONCLUSIONS

Our results suggest that infant metabolite profiles, particularly total and individual lyso.PC.a and NEFA concentrations, were strongly related to growth measures at birth and the odds of adverse birth outcomes. Few individual maternal early-pregnancy metabolites, but not total metabolite concentrations, are associated with foetal growth measures in third trimester and at birth.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Metabolomics to understand placental biology: Where are we now?

Metabolomics, the application of analytical chemistry methodologies to survey the chemical composition of a biological system, is used to globally profile and compare metabolites in one or more groups of samples. Given that metabolites are the terminal end-products of cellular metabolic processes, or 'phenotype' of a cell, tissue, or organism, metabolomics is valuable to the study of the maternal-fetal interface as it has the potential to reveal nuanced complexities of a biological system as well as differences over time or between individuals. The placenta acts as the primary site of maternal-fetal exchange, the success of which is paramount to growth and development of offspring during pregnancy and beyond. Although the study of metabolomics has proven moderately useful for the screening, diagnosis, and understanding of the pathophysiology of pregnancy complications, the placental metabolome in the context of a healthy pregnancy remains poorly characterized and understood. Herein, we discuss the technical aspects of metabolomics and review the current literature describing the placental metabolome in human and animal models, in the context of health and disease. Finally, we highlight areas for future opportunities in the emerging field of placental metabolomics.

Associations between the exposure to organophosphate flame retardants during early pregnancy and the risk of spontaneous abortion based on metabolomics combined with tandem mass spectrometry

BACKGROUND

As potential substitutes for polybrominated diphenyl ethers (PBDEs), organophosphate flame retardants (OPFRs) have been frequently detected in the environment. They have been suggested to impair fetal growth and development in toxicological studies. However, there are few studies on their maternal effects before or during early pregnancy.

METHODS

This study was designed to investigate whether exposure to OPFRs before or during early pregnancy is associated with the risk of spontaneous abortion (SAB), using a nested case-control design based on the case data from clinical examinations in Shanghai, China. A total of 110 cases from this cohort project in 2019-2020 were included. The concentrations of OPFRs in maternal urine samples collected in early pregnancy were determined using Ultra high performance liquid chromatography- triple quadrupole mass spectrometer (UHPLC-MS/MS), and pregnancy outcomes were extracted from the medical records. Meanwhile, ultra high performance liquid chromatography time-of-flight mass spectrometer (UHPLC-Q-TOF/MS)-based metabonomics was used to obtain urine metabolic profiles of 110 women in early pregnancy.

RESULTS

According to the quantitative results, the content of bis(1-chloro-2-propyl)phosphate (BCIPP) in urine was significantly different between the SAB patients and the healthy pregnant women. Besides, metabolic profile analysis showed a significant difference in the urine metabolism profile in early pregnancy between SAB cases and controls. Twenty-five different metabolites were screened out, which showed different degrees of correlation with the urinary BCIPP concentration.

CONCLUSIONS

Based on these results, it suggests that there may be a certain correlation between BCIPP concentration in the urine and the risk of SAB from a metabolomics perspective, and its effect may be related to the metabolism of tryptophan and fatty acids.

Influence of pregnancy and non-fasting conditions on the plasma metabolome in a rat prenatal toxicity study

The current parameters for determining maternal toxicity (e.g. clinical signs, food consumption, body weight development) lack specificity and may underestimate the extent of effects of test compounds on the dams. Previous reports have highlighted the use of plasma metabolomics for an improved and mechanism-based identification of maternal toxicity. To establish metabolite profiles of healthy pregnancies and evaluate the influence of food consumption as a confounding factor, metabolite profiling of rat plasma was performed by gas- and liquid-chromatography-tandem mass spectrometry techniques. Metabolite changes in response to pregnancy, food consumption prior to blood sampling (non-fasting) as well as the interaction of both conditions were studied. In dams, both conditions, non-fasting and pregnancy, had a marked influence on the plasma metabolome and resulted in distinct individual patterns of changed metabolites. Non-fasting was characterized by increased plasma concentrations of amino acids and diet related compounds and lower levels of ketone bodies. The metabolic profile of pregnant rats was characterized by lower amino acids and glucose levels and higher concentrations of plasma fatty acids, triglycerides and hormones, capturing the normal biochemical changes undergone during pregnancy. The establishment of metabolic profiles of pregnant non-fasted rats serves as a baseline to create metabolic fingerprints for prenatal and maternal toxicity studies.

Towards Understanding COVID-19: Molecular Insights, Co-infections, Associated Disorders, and Aging

BACKGROUND

COVID-19 can be related to any diseases caused by microbial infection(s) because 1) co-infection with COVID-19-related virus and other microorganism(s) and 2) because metabolites produced by microorganisms such as bacteria, fungi, and protozoan can be involved in necrotizing pneumonia and other necrotizing medical conditions observed in COVID-19.

OBJECTIVE

By way of illustration, the microbial metabolite of aromatic amino acid tryptophan, a biogenic amine tryptamine inducing neurodegeneration in cell and animal models, also induces necrosis.

METHODS

This report includes analysis of COVID-19 positivity by zip codes in Florida and relation of the positivity to population density, possible effect of ecological and social factors on spread of COVID-19, autopsy analysis of COVID-19 cases from around the world, serum metabolomics analysis, and evaluation of autoantigenome related to COVID-19.

RESULTS

In the present estimations, COVID-19 positivity percent per zip code population varied in Florida from 4.65% to 44.3% (February 2021 data). COVID-19 analysis is partially included in my book Microbial Metabolism and Disease (2021). The autoantigenome related to COVID-19 is characterized by alterations in protein biosynthesis proteins including aminoacyl-tRNA synthetases. Protein biosynthesis alteration is a feature of Alzheimer's disease. Serum metabolomics of COVID-19 positive patients show alteration in shikimate pathway metabolism, which is associated with the presence of Alzheimer's disease-associated human gut bacteria.

CONCLUSION

Such alterations in microbial metabolism and protein biosynthesis can lead to toxicity and neurodegeneration as described earlier in my book Protein Biosynthesis Interference in Disease (2020).

A metabolomics comparison of plant-based meat and grass-fed meat indicates large nutritional differences despite comparable Nutrition Facts panels

A new generation of plant-based meat alternatives-formulated to mimic the taste and nutritional composition of red meat-have attracted considerable consumer interest, research attention, and media coverage. This has raised questions of whether plant-based meat alternatives represent proper nutritional replacements to animal meat. The goal of our study was to use untargeted metabolomics to provide an in-depth comparison of the metabolite profiles a popular plant-based meat alternative (n = 18) and grass-fed ground beef (n = 18) matched for serving size (113 g) and fat content (14 g). Despite apparent similarities based on Nutrition Facts panels, our metabolomics analysis found that metabolite abundances between the plant-based meat alternative and grass-fed ground beef differed by 90% (171 out of 190 profiled metabolites; false discovery rate adjusted p < 0.05). Several metabolites were found either exclusively (22 metabolites) or in greater quantities in beef (51 metabolites) (all, p < 0.05). Nutrients such as docosahexaenoic acid (ω-3), niacinamide (vitamin B3), glucosamine, hydroxyproline and the anti-oxidants allantoin, anserine, cysteamine, spermine, and squalene were amongst those only found in beef. Several other metabolites were found exclusively (31 metabolites) or in greater quantities (67 metabolites) in the plant-based meat alternative (all, p < 0.05). Ascorbate (vitamin C), phytosterols, and several phenolic anti-oxidants such as loganin, sulfurol, syringic acid, tyrosol, and vanillic acid were amongst those only found in the plant-based meat alternative. Large differences in metabolites within various nutrient classes (e.g., amino acids, dipeptides, vitamins, phenols, tocopherols, and fatty acids) with physiological, anti-inflammatory, and/or immunomodulatory roles indicate that these products should not be viewed as truly nutritionally interchangeable, but could be viewed as complementary in terms of provided nutrients. The new information we provide is important for making informed decisions by consumers and health professionals. It cannot be determined from our data if either source is healthier to consume.

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.