Metabolic Biomarkers of Prenatal Disorders: An Exploratory NMR Metabonomics Study of Second Trimester Maternal Urine and Blood Plasma

Subcutaneous adipose tissue compensates for the perturbations in circulating one-carbon metabolism in women with gestational diabetes

The prevalence of gestational diabetes mellitus (GDM) is rising and poses important health risks for the mother, developing fetus and offspring, even when maternal glycemic control is well managed. This study aimed to identify the differently expressed metabolites (DEMs) in maternal plasma between GDM pregnancies with good glycemic control and healthy pregnancies, along with the DEMs-related metabolism in adipose tissue. Pregnant women with scheduled caesarean sections were recruited. Venous blood samples were collected on the day prior to delivery for targeted metabolomics analysis focusing on the 200 polar metabolites in central carbon metabolism. Subcutaneous and omental white adipose tissue (sWAT and oWAT) were harvested at delivery. A total of 162 metabolites were quantified, revealing 2 up-regulated (D-glucose 6-phosphate (G6P), succinate) and 8 down-regulated DEMs, which exhibited a fold change of ≥ 1.5 or ≤ 0.67, respectively. Among the down-regulated DEMs, 5 metabolites-pyridoxine, glycine, S-methyl-L-cysteine, methionine, and S-carboxymethyl-L-cysteine-are related to one-carbon metabolism (OCM). In response to perturbation in circulating OCM, boosted methionine cycle, NAD + metabolism, and adipogenesis were observed in sWAT of GDM subjects, with no changes detected in oWAT. None of the 10 DEMs correlates with either blood glucose or insulin, but showed significant correlations with TG, TC, LDL-C and HDL-C. The present study indicates that sWAT compensates for the perturbations in circulating OCM associated with GDM and targeting to the OCM may be an effective strategy to control the long-term metabolic risk of GDM offsprings.

HPLC-MS/MS method for simultaneous analysis of plasma 2-hydroxybutyrate and 2-hydroxyisobutyrate: Development and clinical significance

Recent studies have identified relationships between diabetes mellitus and short-chain fatty acids, including 2-hydroxybutyrate (2-HB) and 2-hydroxyisobutyrate (2-HiB); 2-HB has been associated to the early stages of insulin resistance, while 2-HiB with the risk and progression of complications of Type 1 diabetes. Their metabolism and pathophysiological role in humans are not fully clarified. The possible association between 2-HB and 2-HiB and diabetes mellitus was investigated with a novel mass spectrometry-based assay, capable of discriminating plasma 2-HiB and 2-HB from their HB isomers. Accuracy and precision (RSD%) were always in the range 99-102% and 0.7-3.5%, respectively. The study involved samples from subjects with normal glucose tolerance (NGT) and Type 2 diabetes (T2D), originally included in a multicenter study investigating mechanisms involved in atherothrombosis. NGT subjects exhibited concentrations of 2-HB and 2-HiB of 61 (36) and 3.1 (1.9) µmol/L, median (interquartile range), respectively, that were significantly lower than those of the T2D patients, whose values were 74 (4.0) and 3.8 (2.9) µmol/L, respectively. The pattern of association of these molecules with clinical and metabolic variables is partially different: both compounds were directly related to male sex, BMI, HbA1c, and plasma glucose, 2-HiB also with age, systolic blood pressure, and HDL-cholesterol. Furthermore, they correlate with free fatty acids, glycerol, and triglyceride concentrations, but the latter correlation was negative for 2-HB and positive for 2-HiB. Results confirmed the clinical significance of 2-HB and 2-HiB, in differential association with metabolic features of T2D.

Association of maternal gut microbial metabolites with gestational diabetes mellitus: evidence from an original case-control study, meta-analysis, and Mendelian randomization

BACKGROUND

The associations of gut microbial metabolites, such as trimethylamine N-oxide (TMAO), its precursors, and phenylacetylglutamine (PAGln), with the risk of gestational diabetes mellitus (GDM) remain unclear.

METHODS

Serum samples of 201 women with GDM and 201 matched controls were collected and then targeted metabolomics was performed to examine the metabolites of interest. Multivariable conditional logistic regression was applied to investigate the relationship between metabolites and GDM. Meta-analysis was performed to combine our results and four similar articles searched from online databases, and Mendelian randomization (MR) analysis was eventually conducted to explore the causalities.

RESULTS

In the case-control study, after dichotomization and comparing the higher versus the lower group, the adjusted odds ratio and 95% confidence interval of choline and L-carnitine with GDM were 2.124 (1.186-3.803) and 0.293 (0.134-0.638), respectively; but neutral relationships between TMAO, betaine, and PAGln with GDM were observed. The following meta-analysis consistently revealed that L-carnitine was negatively associated with GDM. However, MR analyses showed no evidence of causalities.

CONCLUSIONS

Maternal levels of L-carnitine were related to the risk of GDM in both the original case-control study and meta-analysis. However, we did not observe any genetic evidence to establish a causal relationship between this metabolite and GDM.

Urinary biomarkers of preeclampsia: An update

Preeclampsia (PE), a pregnancy-related syndrome, has motivated extensive research to understand its pathophysiology and develop early diagnostic methods. 'Omic' technologies, focusing on genes, mRNA, proteins, and metabolites, have revolutionized biological system studies. Urine emerges as an ideal non-invasive specimen for omics analysis, offering accessibility, easy collection, and stability, making it valuable for identifying biomarkers. A comprehensive exploration of urinary omics in preeclampsia is discussed in this review. Proteomic studies identified biomarkers such as SERPINA-1 and uromodulin, showing promise for early diagnosis and severity assessment. Metabolomic analyses revealed alterations in metabolites like glycine and hippurate, providing insights into molecular mechanisms underlying PE. Challenges include methodological inconsistencies and the need for standardized protocols. Urinary omics technologies have significantly advanced our understanding of PE pathophysiology and hold promise for improved diagnosis and management. Biomarkers identified through these approaches offer potential for early detection, severity stratification, and elucidation of underlying mechanisms.

Serum metabolite profiles of thyroid autoimmunity patients in early pregnancy

Background

Research on serum metabolite profiles in thyroid autoimmunity (TAI) patients during early pregnancy is currently limited.

Aim & Methods

The current study aimed to identify differential serum metabolites and assess the relationship between pregnancy outcomes and metabolic abnormalities in individuals with TAI. This research included 26 pregnant women with TAI and 30 healthy controls (HC). We employed a liquid chromatograph mass spectrometer (LC-MS) to analyze changes between the two groups.

Results

Newborns in the TAI patients had lower birth weights than those in the control group (P = 0.007). We identified 92 differential metabolites (including 50 upregulated and 42 downregulated) belonging to amino acids, fatty acyls, glycerophosphocholines, steroid and other categories and four significantly enrichment Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways including taurine and hypotaurine metabolism, citrate cycle (TCA cycle), glyoxylate and dicarboxylate metabolism and 2-oxocarboxylic acid metabolism. We further identified 15 characteristic metabolites (6-Methylquinoline, D-erythrose 4-phosphate, 4-Hydroxyisoleucine, phosphatidylcholine (PC)(16:2e/16:0), N3,N4-Dimethyl-L-arginine, N-desmethyltramadol, 3-Methoxybenzaldehyde, sphingomyelin (SM)(d14:3/28:2), gamma-Glutamylleucine, NSI-189, 3-(1-cyano-1,2-dihydroisoquinolin-2-yl)-3-oxopropyl propionate, lysophosphatidylinositol (LPI) 16:0, cis-Aconitic acid, polyamide (PA)(18:1/18:2) and fatty acyl esters of hydroxy fatty acid (FAHFA)(17:0/18:0)) using least absolute shrinkage and selection operator (LASSO) regression. Correlation analyses revealed that 6-Methylquinoline, D-erythrose 4-phosphate, gamma-Glutamylleucine, and LPI 16:0 exhibited a positive correlation with anemia before delivery, while 3-(1-cyano-1,2-dihydroisoquinolin-2-yl)-3-oxopropyl propionate had a negative correlation. LPI 16:0 displayed a positive correlation with uric acid (UA) during both middle and late pregnancy, whereas 3-Methoxybenzaldehyde exhibited a negative correlation with UA in late pregnancy. Cis-Aconitic acid showed a positive correlation with fasting blood glucose (FBG) in middle pregnancy. Conversely, 6-Methylquinoline and 4-Hydroxyisoleucine had a negative correlation with birth weight. Thyroid autoantibodies were found to be associated with 14 metabolites identified using LASSO, with the exception of PA (18:1/18:2).

Conclusions

Our findings provide new evidence supporting the early screening of serum metabolites and their potential for predicting adverse pregnancy outcomes in women with TAI.

Titanium exposure and gestational diabetes mellitus: associations and potential mediation by perturbation of amino acids in early pregnancy

BACKGROUND

Several recent studies reported the potential adverse effects of titanium exposure on glucose homeostasis among the non-pregnant population, but the association of titanium exposure with gestational diabetes mellitus (GDM) is scarce.

METHODS

The present study of 1,449 pregnant women was conducted within the Jiangsu Birth Cohort (JBC) study in China. Urine samples were collected in the early pregnancy, and urinary titanium concentration and non-targeted metabolomics were measured. Poisson regression estimated the association of titanium exposure in the early pregnancy with subsequent risk of GDM. Multiple linear regression screened for titanium-related urine metabolites. Mediation analyses assessed the mediating effects of candidate metabolites and pathways.

RESULTS

As parameterized in tertiles, titanium showed positive dose-response relationship with GDM risk (P for trend = 0.008), with women at the highest tertile of titanium exposure having 30% increased risk of GDM [relative risk (RR) = 1.30 (95% CI: 1.06, 1.61)] when compared to those exposure at the first tertile level. Meanwhile, we identified the titanium-related metabolites involved in four amino acid metabolic pathways. Notably, the perturbation of the aminoacyl-tRNA biosynthesis and alanine, aspartate and glutamate metabolism mediated 27.1% and 31.0%, respectively, of the relative effect of titanium exposure on GDM. Specifically, three titanium-related metabolites, choline, creatine and L-alanine, demonstrated predominant mediation effects on the association between titanium exposure and GDM risk.

CONCLUSIONS

In this prospective study, we uniquely identified a correlation between early pregnancy titanium exposure and increased GDM risk. We unveiled novel insights into how perturbations in amino acid metabolism may mediate the link between titanium exposure and GDM. Notably, choline, creatine, and L-alanine emerged as key mediators influencing this association. Our findings imply that elevated titanium exposure in early pregnancy can lead to amino acid dysmetabolism, thereby elevating GDM risk.

Impact of a legumes diet on the human gut microbiome articulated with fecal and plasma metabolomes: A pilot study

BACKGROUND & AIMS

Legumes intake is known to be associated with several health benefits the origins of which is still a matter of debate. This paper addresses a pilot small cohort to probe for metabolic aspects of the interplay between legumes intake, human metabolism and gut microbiota.

METHODS

Untargeted nuclear magnetic resonance (NMR) metabolomics of blood plasma and fecal extracts was carried out, in tandem with qPCR analysis of feces, to assess the impact of an 8-week pilot legumes diet intervention on the fecal and plasma metabolomes and gut microbiota of 19 subjects.

RESULTS

While the high inter-individual variability hindered the detection of statistically significant changes in the gut microbiome, increased fecal glucose and decreased threonine levels were noted. Correlation analysis between the microbiome and fecal metabolome lead to putative hypotheses regarding the metabolic activities of prevalent bacteria groups (Clostridium leptum subgroup, Roseburia spp., and Faecalibacterium prausnitzii). These included elevated fecal glucose as a preferential energy source, the involvement of valerate/isovalerate and reduced protein degradation in gut microbiota. Plasma metabolomics advanced mannose and betaine as potential markers of legume intake and unveiled a decrease in formate and ketone bodies, the latter suggesting improved energy utilization through legume carbohydrates. Amino acid metabolism was also apparently affected, as suggested by lowered urea, histidine and threonine levels.

CONCLUSIONS

Despite the high inter-individual gut microbiome variability characterizing the small cohort addressed, combination of microbiological measurements and untargeted metabolomics unveiled several metabolic effects putatively related to legumes intake. If confirmed in larger cohorts, our findings will support the inclusion of legumes in diets and contribute valuable new insight into the origins of associated health benefits.

Self-Sustaining Triboelectric Nanosensors for Real-Time Urine Analysis in Smart Toilets

Healthcare has undergone a revolutionary shift with the advent of smart technologies, and smart toilets (STs) are among the innovative inventions offering non-invasive continuous health monitoring. The present technical challenges toward this development include limited sensitivity of integrated sensors, poor stability, slow response and the requirement external energy supply alongside manual sample collection. In this article, triboelectric nanosensor array (TENSA) is introduced featuring electrodes crafted from laser-induced 3D graphene with functional polymers like polystyrene, polyimide, and polycaprolactone for real-time urine analysis while generating 50 volts output via urine droplet-based triboelectrification. Though modulating interfacial double-layer capacitance, these sensors exhibit exceptional sensitivity and selectivity in detecting a broad spectrum of urinary biomarkers, including ions, glucose, and urea with a classification precision of 95% and concentration identification accuracy of up to 0.97 (R2), supported by artificial neural networks. Upon exposure to urine samples containing elevated levels of Na+, K+, and NH4 +, a notable decrease (ranging from 32% to 68%) is observed in output voltages. Conversely, urea induces an increase up to 13%. Experimental validation confirms the stability, robustness, reliability, and reproducibility of TENSA, representing a significant advancement in healthcare technology, offering the potential for improved disease management and prevention strategies.

Distinct Plasma Metabolomic and Gut Microbiome Profiles after Gestational Diabetes Mellitus Diet Treatment: Implications for Personalized Dietary Interventions

Gestational diabetes mellitus (GDM) triggers alterations in the maternal microbiome. Alongside metabolic shifts, microbial products may impact clinical factors and influence pregnancy outcomes. We investigated maternal microbiome-metabolomic changes, including over 600 metabolites from a subset of the "Choosing Healthy Options in Carbohydrate Energy" (CHOICE) study. Women diagnosed with GDM were randomized to a diet higher in complex carbohydrates (CHOICE, n = 18, 60% complex carbohydrate/25% fat/15% protein) or a conventional GDM diet (CONV, n = 16, 40% carbohydrate/45% fat/15% protein). All meals were provided. Diets were eucaloric, and fiber content was similar. CHOICE was associated with increases in trimethylamine N-oxide, indoxyl sulfate, and several triglycerides, while CONV was associated with hippuric acid, betaine, and indole propionic acid, suggestive of a healthier metabolome. Conversely, the microbiome of CHOICE participants was enriched with carbohydrate metabolizing genes and beneficial taxa such as Bifidobacterium adolescentis, while CONV was associated with inflammatory pathways including antimicrobial resistance and lipopolysaccharide biosynthesis. We also identified latent metabolic groups not associated with diet: a metabolome associated with less of a decrease in fasting glucose, and another associated with relatively higher fasting triglycerides. Our results suggest that GDM diets produce specific microbial and metabolic responses during pregnancy, while host factors also play a role in triglycerides and glucose metabolism.

Metabolomics of male infertility

This review explores the use of metabolomics in male infertility. Metabolomics, an evolving omics technology that targets the products of cellular metabolism, is valuable for elucidating underlying pathophysiology of many disorders including male infertility. The identification of reliable biomarkers is essential for accurate diagnosis and for developing precision therapeutics for those afflicted by reproductive dysfunction. Unfortunately, despite significant progress to date, the intricate relationships between these metabolic pathways and male infertility remain elusive. It is clear, however, that additional research is required to more fully characterize the role of metabolomics in this disorder and in the potential development of targeted therapies for precision medicine.

Protein lysine four-carbon acylations in health and disease

Lysine acylation, a type of posttranslational protein modification sensitive to cellular metabolic states, influences the functions of target proteins involved in diverse cellular processes. Particularly, lysine butyrylation, crotonylation, β-hydroxybutyrylation, and 2-hydroxyisobutyrylation, four types of four-carbon acylations, are modulated by intracellular concentrations of their respective acyl-CoAs and sensitive to alterations of nutrient metabolism induced by cellular and/or environmental signals. In this review, we discussed the metabolic pathways producing these four-carbon acyl-CoAs, the regulation of lysine acylation and deacylation, and the functions of individual lysine acylation.

Clinical and biochemical associations of urinary metabolites: quantitative epidemiological approach on renal-cardiometabolic biomarkers

BACKGROUND

Urinary metabolomics has demonstrated considerable potential to assess kidney function and its metabolic corollaries in health and disease. However, applications in epidemiology remain sparse due to technical challenges.

METHODS

We added 17 metabolites to an open-access urinary nuclear magnetic resonance metabolomics platform, extending the panel to 61 metabolites (n = 994). We also introduced automated quantification for 11 metabolites, extending the panel to 12 metabolites (+creatinine). Epidemiological associations between these 12 metabolites and 49 clinical measures were studied in three independent cohorts (up to 5989 participants). Detailed regression analyses with various confounding factors are presented for body mass index (BMI) and smoking.

RESULTS

Sex-specific population reference concentrations and distributions are provided for 61 urinary metabolites (419 men and 575 women), together with methodological intra-assay metabolite variations as well as the biological intra-individual and epidemiological population variations. For the 12 metabolites, 362 associations were found. These are mostly novel and reflect potential molecular proxies to estimate kidney function, as the associations cannot be simply explained by estimated glomerular filtration rate. Unspecific renal excretion results in leakage of amino acids (and glucose) to urine in all individuals. Seven urinary metabolites associated with smoking, providing questionnaire-independent proxy measures of smoking status in epidemiological studies. Common confounders did not affect metabolite associations with smoking, but insulin had a clear effect on most associations with BMI, including strong effects on 2-hydroxyisobutyrate, valine, alanine, trigonelline and hippurate.

CONCLUSIONS

Urinary metabolomics provides new insight on kidney function and related biomarkers on the renal-cardiometabolic system, supporting large-scale applications in epidemiology.

Proteins and metabolites fingerprints of gestational diabetes mellitus forming protein-metabolite interactomes are its potential biomarkers

Gestational diabetes mellitus (GDM) is a consequence of glucose intolerance with an inadequate production of insulin that happens during pregnancy and leads to adverse health consequences for both mother and fetus. GDM patients are at higher risk for preeclampsia, and developing diabetes mellitus type 2 in later life, while the child born to GDM mothers are more prone to macrosomia, and hypoglycemia. The universally accepted diagnostic criteria for GDM are lacking, therefore there is a need for a diagnosis of GDM that can identify GDM at its early stage (first trimester). We have reviewed the literature on proteins and metabolites fingerprints of GDM. Further, we have performed protein-protein, metabolite-metabolite, and protein-metabolite interaction network studies on GDM proteins and metabolites fingerprints. Notably, some proteins and metabolites fingerprints are forming strong interaction networks at high confidence scores. Therefore, we have suggested that those proteins and metabolites that are forming protein-metabolite interactomes are the potential biomarkers of GDM. The protein-metabolite biomarkers interactome may help in a deep understanding of the prognosis, pathogenesis of GDM, and also detection of GDM. The protein-metabolites interactome may be further applied in planning future therapeutic strategies to promote long-term health benefits in GDM mothers and their children.

Maternal microbiota and gestational diabetes: impact on infant health

Gestational diabetes mellitus (GDM) is a common complication of pregnancy that has been associated with an increased risk of obesity and diabetes in the offspring. Pregnancy is accompanied by tightly regulated changes in the endocrine, metabolic, immune, and microbial systems, and deviations from these changes can alter the mother's metabolism resulting in adverse pregnancy outcomes and a negative impact on the health of her infant. Maternal microbiomes are significant drivers of mother and child health outcomes, and many microbial metabolites are likely to influence the host health. This review discusses the current understanding of how the microbiota and microbial metabolites may contribute to the development of GDM and how GDM-associated changes in the maternal microbiome can affect infant's health. We also describe microbiota-based interventions that aim to improve metabolic health and outline future directions for precision medicine research in this emerging field.

Metabolomics can provide new insights into perinatal nutrition

Perinatal nutrition is a key factor related to the Developmental Origin of Health and Disease hypothesis, which states that each and every event that happens during the periconceptional period and pregnancy can affect the health status of an individual. Metabolomics can be a very useful tool for gathering information about the effect of perinatal nutrition on both mothers and newborn infants. This non-systematic review focuses on the main metabolites detected by this technique, with regard to gestational diabetes, intrauterine growth restriction and breast milk. Conclusion. Nutrition, metabolome and microbiome interactions are gaining interest in the scientific community.

2-hydroxyisobutyric acid (2-HIBA) modulates ageing and fat deposition in Caenorhabditis elegans

High levels of 2-hydroxyisobutyric acid (2-HIBA) were found in urines of patients with obesity and hepatic steatosis, suggesting a potential involvement of this metabolite in clinical conditions. The gut microbial origin of 2-HIBA was hypothesized, however its actual origin and role in biological processes are still not clear. We investigated how treatment with 2-HIBA affected the physiology of the model organism Caenorhabditis elegans, in both standard and high-glucose diet (HGD) growth conditions, by targeted transcriptomic and metabolomic analyses, Coherent Anti-Stokes Raman Scattering (CARS) and two-photon fluorescence microscopy. In standard conditions, 2-HIBA resulted particularly effective to extend the lifespan, delay ageing processes and stimulate the oxidative stress resistance in wild type nematodes through the activation of insulin/IGF-1 signaling (IIS) and p38 MAPK pathways and, consequently, through a reduction of ROS levels. Moreover, variations of lipid accumulation observed in treated worms correlated with transcriptional levels of fatty acid synthesis genes and with the involvement of peptide transporter PEP-2. In HGD conditions, the effect of 2-HIBA on C. elegans resulted in a reduction of the lipid droplets deposition, accordingly with an increase of acs-2 gene transcription, involved in β-oxidation processes. In addition, the pro-longevity effect appeared to be correlated to higher levels of tryptophan, which may play a role in restoring the decreased viability observed in the HGD untreated nematodes.

Maternal Physiological Variations Induced by Chronic Gestational Hypoxia: 1H NMR-Based Metabolomics Study

Metabolomics have been widely used in pregnancy-related diseases. However, physiological variations induced by chronic hypoxia during pregnancy are not well characterized. We aimed to investigate physiological variations induced by chronic hypoxia during pregnancy. A Sprague-Dawley (SD) pregnant rat model of chronic hypoxia was established. Plasma and urine metabolite profiles at different stages of the pregnancy were detected by ¹H NMR (nuclear magnetic resonance). Multivariate statistical analysis was used to analyze changes in plasma and urine metabolic trajectories at different time-points. We identified hypoxia-induced changes in the levels of 30 metabolites in plasma and 29 metabolites in urine during different stages of pregnancy; the prominently affected metabolites included acetic acid, acetone, choline, citric acid, glutamine, isoleucine, lysine, and serine. Most significant hypoxia-induced changes in plasma and urine sample metabolites were observed on the 11th day of gestation. In summary, chronic hypoxia has a significant effect on pregnant rats, and may cause metabolic disorders involving glucose, lipids, amino acids, and tricarboxylic acid cycle. Metabolomics study of the effect of hypoxia during pregnancy may provide insights into the pathogenesis of obstetric disorders.

Predictive Metabolomic Markers in Early to Mid-pregnancy for Gestational Diabetes Mellitus: A Prospective Test and Validation Study

Gestational diabetes mellitus (GDM) predisposes pregnant individuals to perinatal complications and long-term diabetes and cardiovascular diseases. We developed and validated metabolomic markers for GDM in a prospective test-validation study. In a case-control sample within the PETALS cohort (GDM n = 91 and non-GDM n = 180; discovery set), a random PETALS subsample (GDM n = 42 and non-GDM n = 372; validation set 1), and a case-control sample within the GLOW trial (GDM n = 35 and non-GDM n = 70; validation set 2), fasting serum untargeted metabolomics were measured by gas chromatography/time-of-flight mass spectrometry. Multivariate enrichment analysis examined associations between metabolites and GDM. Ten-fold cross-validated LASSO regression identified predictive metabolomic markers at gestational weeks (GW) 10-13 and 16-19 for GDM. Purinone metabolites at GW 10-13 and 16-19 and amino acids, amino alcohols, hexoses, indoles, and pyrimidine metabolites at GW 16-19 were positively associated with GDM risk (false discovery rate <0.05). A 17-metabolite panel at GW 10-13 outperformed the model using conventional risk factors, including fasting glycemia (area under the curve: discovery 0.871 vs. 0.742, validation 1 0.869 vs. 0.731, and validation 2 0.972 vs. 0.742; P < 0.01). Similar results were observed with a 13-metabolite panel at GW 17-19. Dysmetabolism is present early in pregnancy among individuals progressing to GDM. Multimetabolite panels in early pregnancy can predict GDM risk beyond conventional risk factors.

Maternal Dietary Betaine Prevents High-Fat Diet-Induced Metabolic Disorders and Gut Microbiota Alterations in Mouse Dams and Offspring From Young to Adult

Early life is a critical window for preventing the intergenerational transmission of metabolic diseases. Betaine has been proven to play a role in improving glucose and lipid metabolism disorders in animal models. However, whether maternal betaine supplementation plays a role in regulating gut microbiota in both dams and offspring remains unclear. In this study, C57BL/6 female mice were fed with control diet (Ctr), high-fat diet (HF), and high-fat with betaine supplementation (0.3% betaine in the diet, HFB) from 3 weeks prior to mating and lasted throughout pregnancy and lactation. After weaning, the offspring got free access to normal chow diet until 20 weeks of age. We found that maternal dietary betaine supplementation significantly improved glucose and insulin resistance, as well as reduced free fatty acid (FFA) concentration in dams and offspring from young to adult. When compared to the HF group, Intestinimonas and Acetatifactor were reduced by betaine supplementation in dams; Desulfovibrio was reduced in 4-week-old offspring of the HFB group; and Lachnoclostridium was enriched in 20-week-old offspring of the HFB group. Moreover, the persistent elevated genus Romboutsia in both dams and offspring in the HFB group was reported for the first time. Overall, maternal betaine could dramatically alleviate the detrimental effects of maternal overnutrition on metabolism in both dams and offspring. The persistent alterations in gut microbiota might play critical roles in uncovering the intergenerational metabolic benefits of maternal betaine, which highlights evidence for combating generational metabolic diseases.

Uncontrolled Thyroid during Pregnancy Alters the Circulative and Exerted Metabolome

Normal levels of thyroid hormones (THs) are essential for a normal pregnancy outcome, fetal growth and the normal function of the central nervous system. Hypothyroidism, a common endocrine disorder during pregnancy, is a significant metabolic factor leading to cognitive impairments. It is essential to investigate whether patients with thyroid dysfunction may present an altered circulative and excreted metabolic profile, even after receiving treatment with thyroxine supplements. NMR metabolomics was employed to analyze 90 serum and corresponding colostrum samples. Parallel analyses of the two biological specimens provided a snapshot of the maternal metabolism through the excretive and circulating characteristics of mothers. The metabolomics data were analyzed by performing multivariate statistical, biomarker and pathway analyses. Our results highlight the impact of hypothyroidism on metabolites’ composition during pregnancy and lactation. Thyroid disorder causing metabolite fluctuations may lead to impaired lipid and glucose metabolic pathways as well as aberrant prenatal neurodevelopment, thus posing a background for the occurrence of metabolic syndrome or neurogenerative diseases later in life. This risk applies to not only untreated but also hypothyroid women under replacement therapy since our findings in both biofluids framed a different metabolic phenotype for the latter group, thus emphasizing the need to monitor women adequately after treatment initiation.

Systematic review of preterm birth multi-omic biomarker studies

Preterm birth (PTB) is one of the leading causes of deaths in infants under the age of five. Known risk factors of PTB include genetic factors, lifestyle choices or infection. Identification of omic biomarkers associated with PTB could aid clinical management of women at high risk of early labour and thereby reduce neonatal morbidity. This systematic literature review aimed to identify and summarise maternal omic and multi-omic (genomics, transcriptomics, proteomics and metabolites) biomarker studies of PTB. Original research articles were retrieved from three databases: PubMed, Web of Science and Science Direct, using specified search terms for each omic discipline. PTB studies investigating genomics, transcriptomics, proteomics or metabolomics biomarkers prior to onset of labour were included. Data were collected and reviewed independently. Pathway analyses were completed on the biomarkers from non-targeted omic studies using Reactome pathway analysis tool. A total of 149 omic studies were identified; most of the literature investigated proteomic biomarkers. Pathway analysis identified several cellular processes associated with the omic biomarkers reported in the literature. Study heterogeneity was observed across the research articles, including the use of different gestation cut-offs to define PTB. Infection/inflammatory biomarkers were identified across majority of papers using a range of targeted and non-targeted approaches.

Alterations of gut microbiota-derived metabolites in gestational diabetes mellitus and clinical significance

BACKGROUND

The change in the characteristics of the gut microbiota is linked to gestational diabetes mellitus (GDM). However, whether and how the gut microbiota-derived metabolites change in GDM is uncertain. Here, we aimed to determine associations between the gut microbiota-derived metabolites and GDM.

METHODS

Using targeted metabolomics approaches, 7 types of short-chain fatty acids (SCFAs), 38 types of bile acids (BAs), and 5 types of trimethylamine N-oxide (TMAO), and its derivatives of serum samples were obtained from pregnant women with GDM (n = 24), and healthy pregnant controls (HC, n = 28) were detected to identify the metabolic signature of GDM to investigate the potential biomarkers. Moreover, we assessed the associations between gut microbiota-derived metabolites and clinical parameters.

RESULTS

In our study, the gut microbiota-derived metabolites signatures were significantly different between GDM and HC. Quantitative results showed the levels of isobutyric acid, isovaleric acid, valeric acid, caproic acid, GUDCA, THDCA + TUDCA, and LCA-3S were significantly higher in GDM, but the level of TMAO and its derivatives did not change significantly. Some altered gut microbiota-derived metabolites were significantly correlated with glucose and lipid levels. Receiver-operating characteristic (ROC) analysis of generalized linear models showed that gut microbiota-derived metabolites may be potential biomarkers of GDM.

CONCLUSION

This study highlights gut microbiota-derived metabolites alterations in GDM and correlation of the clinical indicators, which provides a new direction for future studies aiming to novel serum biomarker for early detection or target of drug therapy of GDM.

Adaptive Changes in Glucose Homeostasis and Islet Function During Pregnancy: A Targeted Metabolomics Study in Mice

OBJECTIVE

Pregnancy is a dynamic state involving multiple metabolic adaptions in various tissues including the endocrine pancreas. However, a detailed characterization of the maternal islet metabolome in relation to islet function and the ambient circulating metabolome during pregnancy has not been established.

METHODS

A timed-pregnancy mouse model was studied, and age-matched non-pregnant mice were used as controls. Targeted metabolomics was applied to fasting plasma and purified islets during each trimester of pregnancy. Glucose homeostasis and islet function was assessed. Bioinformatic analyses were performed to reveal the metabolic adaptive changes in plasma and islets, and to identify key metabolic pathways associated with pregnancy.

RESULTS

Fasting glucose and insulin were found to be significantly lower in pregnant mice compared to non-pregnant controls, throughout the gestational period. Additionally, pregnant mice had superior glucose excursions and greater insulin response to an oral glucose tolerance test. Interestingly, both alpha and beta cell proliferation were significantly enhanced in early to mid-pregnancy, leading to significantly increased islet size seen in mid to late gestation. When comparing the plasma metabolome of pregnant and non-pregnant mice, phospholipid and fatty acid metabolism pathways were found to be upregulated throughout pregnancy, whereas amino acid metabolism initially decreased in early through mid pregnancy, but then increased in late pregnancy. Conversely, in islets, amino acid metabolism was consistently enriched throughout pregnancy, with glycerophospholid and fatty acid metabolism was only upregulated in late pregnancy. Specific amino acids (glutamate, valine) and lipids (acyl-alkyl-PC, diacyl-PC, and sphingomyelin) were found to be significantly differentially expressed in islets of the pregnant mice compared to controls, which was possibly linked to enhanced insulin secretion and islet proliferation.

CONCLUSION

Beta cell proliferation and function are elevated during pregnancy, and this is coupled to the enrichment of islet metabolites and metabolic pathways primarily associated with amino acid and glycerophospholipid metabolism. This study provides insight into metabolic adaptive changes in glucose homeostasis and islet function seen during pregnancy, which will provide a molecular rationale to further explore the regulation of maternal metabolism to avoid the onset of pregnancy disorders, including gestational diabetes.

Perspectives from metabolomics in the early diagnosis and prognosis of gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is one of the most common metabolic disorders in pregnant women. The early detection of GDM provides an opportunity for the effective treatment of hyperglycemia in pregnancy, thus decreasing the risk of adverse perinatal outcomes for mothers and newborns. Metabolomics, an emerging technique, offers a novel point of view in understanding the onset and development of diseases and has been repeatedly used in various gestational periods in recent studies of GDM. Moreover, metabolomics provides varied opportunities in the different diagnoses of GDM from prediabetes or predisposition to diabetes, the diagnosis of GDM at a gestational age several weeks earlier than that used in the traditional method, and the assessment of prognosis considering the physiologic subtypes of GDM and clinical indexes. Longitudinal metabolomics truly facilitates the dynamic monitoring of metabolic alterations over the course of pregnancy. Herein, we review recent advancements in metabolomics and summarize evidence from studies on the application of metabolomics in GDM, highlighting the aspects of the diagnosis and differential diagnoses of GDM in an early stage. We also discuss future study directions concerning the physiologic subtypes, prognosis, and limitations of metabolomics.

Predictive values of serum metabolites in early pregnancy and their possible pathways for gestational diabetes: A nested case-control study in Tianjin, China

AIMS

To investigate the associations and predictive values of serum metabolites in early pregnancy for later development of gestational diabetes mellitus (GDM), and further explore their metabolic pathways to GDM.

METHODS

We conducted a 1:1 nested case-control study including 486 pregnant women from Tianjin, China, and collected blood samples at their first registration (median at 10th gestational week). Liquid chromatography-tandem mass spectrometry was used to measure serum metabolites. Orthogonal partial least squares discriminant analysis was used to select specific metabolites associated with GDM, and pathway analysis was used to identify the metabolic pathways related to GDM.

RESULTS

A total of 64 serum metabolites were included in this analysis, 17 of which were identified as specific metabolites associated with GDM. Ten metabolites increased and seven metabolites decreased GDM risk. Inclusion of these specific metabolites to the model of traditional risk factors greatly increased the predictive value from 0.69 (95% confidence interval: 0.64-0.74) to 0.92 (0.90-0.95). In addition, we found that glycerophospholipid metabolism, sphingolipid metabolism and primary bile acid biosynthesis were main metabolic pathways related to GDM.

CONCLUSION

We identified a set of serum metabolites and their metabolic pathways in early pregnancy associated with GDM, which provided a theoretical basis for further research on the molecular pathways to GDM and early identification of GDM.

Analysis of urinary organic acids by gas chromatography tandem mass spectrometry method for metabolic profiling applications

A sensitive, accurate and precise method was developed for the quantification of a large number of organic acids in human urine by GC-MS/MS. The analytes were selected based on their role as key metabolic intermediates; intermediates of Krebs cycle, fatty acid oxidation, glycolysis, down-stream metabolites of neurotransmitter synthesis and degradation, metabolites indicative of nutritional deficiencies, byproducts of microbial activity in the gastrointestinal tract (GI) etc. The most efficient sample preparation protocol was selected based on tests for extraction with different solvents such as MTBE and ethyl acetate under acidic conditions, whereas finally a more general protocol was applied with methanol. Regarding derivatization, methoxyamine with MSTFA, 1% TMCS was applied. The method was extensively validated, including stability study, ensuring accurate determination of the studied organic acids in human urine. Proof of its utility was exhibited in a set of samples from human volunteers. The method can find wide applicability in the context of metabolomics for clinical or nutritional studies.

Metabolic profiling of maternal serum of women at high-risk of spontaneous preterm birth using NMR and MGWAS approach

Preterm birth (PTB) is a leading global cause of infant mortality. Risk factors include genetics, lifestyle choices and infection. Understanding the mechanism of PTB could aid the development of novel approaches to prevent PTB. This study aimed to investigate the metabolic biomarkers of PTB in early pregnancy and the association of significant metabolites with participant genotypes. Maternal sera collected at 16 and 20 weeks of gestation, from women who previously experienced PTB (high-risk) and women who did not (low-risk controls), were analysed using 1H nuclear magnetic resonance (NMR) metabolomics and genome-wide screening microarray. ANOVA and probabilistic neural network (PNN) modelling were performed on the spectral bins. Metabolomics genome-wide association (MGWAS) of the spectral bins and genotype data from the same participants was applied to determine potential metabolite-gene pathways. Phenylalanine, acetate and lactate metabolite differences between PTB cases and controls were obtained by ANOVA and PNN showed strong prediction at week 20 (AUC = 0.89). MGWAS identified several metabolite bins with strong genetic associations. Cis-eQTL analysis highlighted TRAF1 (involved in the inflammatory pathway) local to a non-coding SNP associated with lactate at week 20 of gestation. MGWAS of a well-defined cohort of participants highlighted a lactate-TRAF1 relationship that could potentially contribute to PTB.

First trimester metabolomics 1H-NMR study of the urinary profile predicts gestational diabetes mellitus development in obese women

OBJECTIVE

Obesity is one of the main risk factors for the development gestational diabetes mellitus (GDM). Thus, we aim to identify changes in the urinary metabolomics profile of obese women at first trimester of pregnancy in order to predict later GDM diagnosis.

RESEARCH DESIGN AND METHODS

In this nested case-control study, urine samples collected in the first trimester of pregnancy obtained from obese women who developed GDM (n = 29) and obese women who did not develop diabetes (n = 25 NO GDM) were analyzed with Nuclear Magnetic Resonance spectroscopy combined with Multivariate Statistical Analysis. GDM diagnosis was obtained with one-step oral glucose load.

RESULTS

OPLS-DA significantly separated the GDM women from NO GDM women. Specifically, GDM women were characterized by a higher level of tryptophan, trigonelline, hippurate, and threonine, and lower levels of 1-methylnicotinamide, 3-hydroxykynurenine, glycocholate, isoleucine, kynurenine, and valine compared to NO GDM women.

CONCLUSION

In a prevalently Caucasian population, the changes of some metabolites such as tryptophan, trigonelline, and branch-chained amino acids in the urinary profile of obese women in the first trimester are able to make unequivocal prediction of those which later test positive for GDM. This approach could be useful to diagnose much earlier obese women with GDM allowing lifestyle counselling and other interventions.

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.

Molecular Mechanism of Microbiota Metabolites in Preterm Birth: Pathological and Therapeutic Insights

Preterm birth (PTB) refers to the birth of infants before 37 weeks of gestation and is a challenging issue worldwide. Evidence reveals that PTB is a multifactorial dysregulation mediated by a complex molecular mechanism. Thus, a better understanding of the complex molecular mechanisms underlying PTB is a prerequisite to explore effective therapeutic approaches. During early pregnancy, various physiological and metabolic changes occur as a result of endocrine and immune metabolism. The microbiota controls the physiological and metabolic mechanism of the host homeostasis, and dysbiosis of maternal microbial homeostasis dysregulates the mechanistic of fetal developmental processes and directly affects the birth outcome. Accumulating evidence indicates that metabolic dysregulation in the maternal or fetal membranes stimulates the inflammatory cytokines, which may positively progress the PTB. Although labour is regarded as an inflammatory process, it is still unclear how microbial dysbiosis could regulate the molecular mechanism of PTB. In this review based on recent research, we focused on both the pathological and therapeutic contribution of microbiota-generated metabolites to PTB and the possible molecular mechanisms.

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.

Metabolomic Biomarkers in Gestational Diabetes Mellitus: A Review of the Evidence

Gestational diabetes mellitus (GDM) is the fastest growing type of diabetes, affecting between 2 to 38% of pregnancies worldwide, varying considerably depending on diagnostic criteria used and sample population studied. Adverse obstetric outcomes include an increased risk of macrosomia, and higher rates of stillbirth, instrumental delivery, and birth trauma. Metabolomics, which is a platform used to analyse and characterise a large number of metabolites, is increasingly used to explore the pathophysiology of cardiometabolic conditions such as GDM. This review aims to summarise metabolomics studies in GDM (from inception to January 2021) in order to highlight prospective biomarkers for diagnosis, and to better understand the dysfunctional metabolic pathways underlying the condition. We found that the most commonly deranged pathways in GDM include amino acids (glutathione, alanine, valine, and serine), carbohydrates (2-hydroxybutyrate and 1,5-anhydroglucitol), and lipids (phosphatidylcholines and lysophosphatidylcholines). We also highlight the possibility of using certain metabolites as predictive markers for developing GDM, with the use of highly stratified modelling techniques. Limitations for metabolomic research are evaluated, and future directions for the field are suggested to aid in the integration of these findings into clinical practice.

Predictive role of altered leptin, adiponectin and 3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid secretion in gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is one of the most common complications of pregnancy, leading to considerable maternal and fetal risks. The main aim of this study was to determine the predictive value of the levels of adiponectin (AN), leptin (L) and CMPF (3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid) in the development of GDM. We conducted a prospective longitudinal study on 68 pregnant women that were not at risk of developing GDM, in whom we determined AN, L, CMPF levels at 11-13 weeks +6 days of pregnancy during the first trimester screening. Twenty-one of all the patients included in the study developed GDM during pregnancy. Oral glucose tolerance test (OGTT)/75 g was performed at 24-28 weeks of gestation. L levels were significantly higher in patients who developed GDM than in those who did not develop diabetes (P<0.001). The AN/L ratio was significantly lower in patients with GDM (P=0.03). AN and CMPF levels were not associated with GDM. The probability of developing gestational diabetes was higher in patients with L levels above the L cut-off value of 16 ng/ml [area under the curve (AUC), 0.775; 95% confidence interval (CI) 0.658-0.867], sensitivity 100% (95% CI 83.9-100), specificity 48.9% (95% CI 34.1-63.9) (P<0.001). Advanced maternal age and higher L levels were found to be predictive factors [odds ratio (OR)=1.16 and OR=1.06, respectively] independently associated with gestational diabetes. In as far as general factors are concerned, the patient BMI (body mass index) at the beginning of the pregnancy and smoking were found to be the main risk factors for the onset of GDM. This study showed that elevated L levels are a strong predictor of GDM, while AN and CMPF levels are not, as they failed to show a significant association.

Maternal Plasma Betaine in Middle Pregnancy Was Associated with Decreased Risk of GDM in Twin Pregnancy: A Cohort Study

PURPOSE

Although previous studies have shown that choline-related metabolites in one carbon metabolism (OCM) were related to gestational diabetes mellitus (GDM) risk in singleton pregnancy, their role in twin gestations remains unclear. We aimed to investigate the associations between choline, betaine, methionine, dimethylglycine (DMG), trimethylamine N-oxide (TMAO) and GDM risk among women with twin gestations.

PATIENTS AND METHODS

This hospital-based cohort study included 187 women with dichorionic twin gestations. Blood samples were collected during pregnancy at a median of 16.1 weeks of gestation (IQR: 13.9 -17.9). Concentrations of plasma metabolites were measured by HPLC-triple quadrupole MS. Log-binomial regression models were applied to estimate the associations between plasma metabolites and the risk of GDM.

RESULTS

A total of 57 (30.5%) GDM cases were diagnosed over the study follow-up. Eighty-seven percent of women conceived through ART. Plasma betaine had an inverse association with GDM risk, and the adjusted RR of GDM comparing the highest tertile with the lowest tertile was 0.41 (95% CI: 0.19-0.86, P _trend=0.015). Women with a high betaine/choline ratio or a low DMG/betaine ratio were at decreased GDM risk (P _trend=0.031 or 0.001, respectively). Plasma choline, methionine, DMG and TMAO were not associated with GDM risk.

CONCLUSION

Among women with dichorionic twin gestations, higher plasma level of betaine in the second trimester was associated with lower risk of GDM. This finding needs further confirmation.

Maternal plasma metabolic markers of neonatal adiposity and associated maternal characteristics: The GUSTO study

Infant adiposity may be related to later metabolic health. Maternal metabolite profiling reflects both genetic and environmental influences and allows elucidation of metabolic pathways associated with infant adiposity. In this multi-ethnic Asian cohort, we aimed to (i) identify maternal plasma metabolites associated with infant adiposity and other birth outcomes and (ii) investigate the maternal characteristics associated with those metabolites. In 940 mother-offspring pairs, we performed gas chromatography-mass spectrometry and identified 134 metabolites in maternal fasting plasma at 26–28 weeks of gestation. At birth, neonatal triceps and subscapular skinfold thicknesses were measured by trained research personnel, while weight and length measures were abstracted from delivery records. Gestational age was estimated from first-trimester dating ultrasound. Associations were assessed by multivariable linear regression, with p-values corrected using the Benjamini-Hochberg approach. At a false discovery rate of 5%, we observed associations between 28 metabolites and neonatal sum of skinfold thicknesses (13 amino acid-related, 4 non-esterified fatty acids, 6 xenobiotics, and 5 unknown compounds). Few associations were observed with gestational duration, birth weight, or birth length. Maternal ethnicity, pre-pregnancy BMI, and diet quality during pregnancy had the strongest associations with the specific metabolome related to infant adiposity. Further studies are warranted to replicate our findings and to understand the underlying mechanisms.

Chromatin as a key consumer in the metabolite economy

In eukaryotes, chromatin remodeling and post-translational modifications (PTMs) shape the local chromatin landscape to establish permissive and repressive regions within the genome, orchestrating transcription, replication, and DNA repair in concert with other epigenetic mechanisms. Though cellular nutrient signaling encompasses a huge number of pathways, recent attention has turned to the hypothesis that the metabolic state of the cell is communicated to the genome through the type and concentration of metabolites in the nucleus that are cofactors for chromatin-modifying enzymes. Importantly, both epigenetic and metabolic dysregulation are hallmarks of a range of diseases, and this metabolism-chromatin axis may yield a well of new therapeutic targets. In this Perspective, we highlight emerging themes in the inter-regulation of the genome and metabolism via chromatin, including nonenzymatic histone modifications arising from chemically reactive metabolites, the expansion of PTM diversity from cofactor-promiscuous chromatin-modifying enzymes, and evidence for the existence and importance of subnucleocytoplasmic metabolite pools.

Whole-Genome Promoter Profiling of Plasma DNA Exhibits Diagnostic Value for Placenta-Origin Pregnancy Complications

Placenta-origin pregnancy complications, including preeclampsia (PE), gestational diabetes mellitus (GDM), fetal growth restriction (FGR), and macrosomia (MA) are common occurrences in pregnancy, resulting in significant morbidity and mortality for both mother and fetus. However, despite their frequency, there are no reliable methods for the early diagnosis of these complications. Since cfDNA is mainly derived from placental trophoblasts and maternal hematopoietic cells, it might have information for gene expression which can be used for disease prediction. Here, low coverage whole-genome sequencing on plasma DNA from 2,199 pregnancies is performed based on retrospective cohorts of 3,200 pregnant women. Read depth in the promoter regions is examined to define read-depth distribution patterns of promoters for pregnancy complications and controls. Using machine learning methods, classifiers for predicting pregnancy complications are developed. Using these classifiers, complications are successfully predicted with an accuracy of 80.3%, 78.9%, 72.1%, and 83.0% for MA, FGR, GDM, and PE, respectively. The findings suggest that promoter profiling of cfDNA may be used as a biological biomarker for predicting pregnancy complications at early gestational age.

Vaginal metabolome: towards a minimally invasive diagnosis of microbial invasion of the amniotic cavity in women with preterm labor

Microbial invasion of the amniotic cavity (MIAC) is only identified by amniocentesis, an invasive procedure that limits its clinical translation. Here, we aimed to evaluate whether the vaginal metabolome discriminates the presence/absence of MIAC in women with preterm labor (PTL) and intact membranes. We conducted a case-control study in women with symptoms of PTL below 34 weeks who underwent amniocentesis to discard MIAC. MIAC was defined as amniotic fluid positive for microorganisms identified by specific culture media. The cohort included 16 women with MIAC and 16 control (no MIAC). Both groups were matched for age and gestational age at admission. Vaginal fluid samples were collected shortly after amniocentesis. Metabolic profiles were analyzed by nuclear magnetic resonance (NMR) spectroscopy and compared using multivariate and univariate statistical analyses to identify significant differences between the two groups. The vaginal metabolomics profile of MIAC showed higher concentrations of hypoxanthine, proline, choline and acetylcholine and decreased concentrations of phenylalanine, glutamine, isoleucine, leucine and glycerophosphocholine. In conclusion, metabolic changes in the NMR-based vaginal metabolic profile are able to discriminate the presence/absence of MIAC in women with PTL and intact membranes. These metabolic changes might be indicative of enhanced glycolysis triggered by hypoxia conditions as a consequence of bacterial infection, thus explaining the utilization of alternative energy sources in an attempt to replenish glucose.

Integrated metabolome analysis reveals novel connections between maternal fecal metabolome and the neonatal blood metabolome in women with gestational diabetes mellitus

Gestational Diabetes Mellitus (GDM), which is correlated with changes in the gut microbiota, is a risk factor for neonatal inborn errors of metabolism (IEMs). Maternal hyperglycemia exerts epigenetic effects on genes that encode IEM-associated enzymes, resulting in changes in the neonatal blood metabolome. However, the relationship between maternal gut microbiota and the neonatal blood metabolome remains poorly understood. This study aimed at understanding the connections between maternal gut microbiota and the neonatal blood metabolome in GDM. 1H-NMR-based untargeted metabolomics was performed on maternal fecal samples and targeted metabolomics on the matched neonatal dry blood spots from a cohort of 40 pregnant women, including 22 with GDM and 18 controls. Multi-omic association methods (including Co-Inertia Analysis and Procrustes Analysis) were applied to investigate the relationship between maternal fecal metabolome and the neonatal blood metabolome. Both maternal fecal metabolome and the matched neonatal blood metabolome could be separated along the vector of maternal hyperglycemia. A close relationship between the maternal and neonatal metabolomes was observed by multi-omic association approaches. Twelve out of thirty-two maternal fecal metabolites with altered abundances from 872 1H- NMR features (Bonferroni-adjusted P < 0.05) in women with GDM and the controls were identified, among which 8 metabolites contribute (P < 0.05 in a 999-step permutation test) to the close connection between maternal and the neonatal metabolomes in GDM. Four of these eight maternal fecal metabolites, including lysine, putrescine, guanidinoacetate, and hexadecanedioate, were negatively associated (Spearman rank correlation, coefficient value < −0.6, P < 0.05) with maternal hyperglycemia. Biotin metabolism was enriched (Bonferroni-adjusted P < 0.05 in the hypergeometric test) with the four-hyperglycemia associated fecal metabolites. The results of this study suggested that maternal fecal metabolites contribute to the connections between maternal fecal metabolome and the neonatal blood metabolome and may further affect the risk of IEMs.

A prospective study of associations between in utero exposure to gestational diabetes mellitus and metabolomic profiles during late childhood and adolescence

AIMS/HYPOTHESIS

This study aimed to: (1) identify metabolite patterns during late childhood that differ with respect to exposure to maternal gestational diabetes mellitus (GDM); (2) examine the persistence of GDM/metabolite associations 5 years later, during adolescence; and (3) investigate the associations of metabolite patterns with adiposity and metabolic biomarkers from childhood through adolescence.

METHODS

This study included 592 mother-child pairs with information on GDM exposure (n = 92 exposed), untargeted metabolomics data at age 6-14 years (T1) and at 12-19 years (T2), and information on adiposity and metabolic risk biomarkers at T1 and T2. We first consolidated 767 metabolites at T1 into factors (metabolite patterns) via principal component analysis (PCA) and used multivariable regression to identify factors that differed by GDM exposure, at α = 0.05. We then examined associations of GDM with individual metabolites within factors of interest at T1 and T2, and investigated associations of GDM-related factors at T1 with adiposity and metabolic risk throughout T1 and T2 using mixed-effects linear regression models.

RESULTS

Of the six factors retained from PCA, GDM exposure was associated with greater odds of being in quartile (Q)4 (vs Q1-3) of 'Factor 4' at T1 after accounting for age, sex, race/ethnicity, maternal smoking habits during pregnancy, Tanner stage, physical activity and total energy intake, at α = 0.05 (OR 1.78 [95% CI 1.04, 3.04]; p = 0.04). This metabolite pattern comprised phosphatidylcholines, diacylglycerols and phosphatidylethanolamines. GDM was consistently associated with elevations in a subset of individual compounds within this pattern at T1 and T2. While this metabolite pattern was not related to the health outcomes in boys, it corresponded with greater adiposity and a worse metabolic profile among girls throughout the follow-up period. Each 1-unit increment in Factor 4 corresponded with 0.17 (0.08, 0.25) units higher BMI z score, 8.83 (5.07, 12.59) pmol/l higher fasting insulin, 0.28 (0.13, 0.43) units higher HOMA-IR, and 4.73 (2.15, 7.31) nmol/l higher leptin.

CONCLUSIONS/INTERPRETATION

Exposure to maternal GDM was nominally associated with a metabolite pattern characterised by elevated serum phospholipids in late childhood and adolescence at α = 0.05. This metabolite pattern was associated with greater adiposity and metabolic risk among female offspring throughout the late childhood-to-adolescence transition. Future studies are warranted to confirm our findings.

Genetics and Epigenetics: New Insight on Gestational Diabetes Mellitus

Gestational diabetes mellitus (GDM) is the most common metabolic complication of pregnancy, with a prevalence that has increased significantly in the last decade, coming to affect 12-18% of all pregnancies. GDM is believed to be the result of a combination of genetic, epigenetic and environmental factors. Following the identification of susceptibility genes for type 2 diabetes by means of genome-wide association studies, an association has also been demonstrated between some type 2 diabetes susceptibility genes and GDM, suggesting a partial similarity of the genetic architecture behind the two forms of diabetes. More recent genome-wide association studies, focusing on maternal metabolism during pregnancy, have demonstrated an overlap in the genes associated with metabolic traits in gravid and non-gravid populations, as well as in genes apparently unique to pregnancy. Epigenetic changes-such as DNA methylation, histone modifications and microRNA gene silencing-have also been identified in GDM patients. Metabolomics has been used to profile the metabolic state of women during pregnancy, based on the measurement of numerous low-molecular-weight metabolites. Measuring amino acids and conventional metabolites has revealed changes in pregnant women with a higher insulin resistance and high blood glucose levels that resemble the changes seen in non-gravid, insulin-resistant populations. This would suggest similarities in the metabolic profiles typical of insulin resistance and hyperglycemia whether individuals are pregnant or not. Future studies combining data obtained using multiple technologies will enable an integrated systems biology approach to maternal metabolism during a pregnancy complicated by GDM. This review highlights the recent knowledge on the impact of genetics and epigenetics in the pathophysiology of GDM and the maternal and fetal complications associated with this pathology condition.

Longitudinal Changes of One-Carbon Metabolites and Amino Acid Concentrations during Pregnancy in the Women First Maternal Nutrition Trial

BACKGROUND

Maternal dietary restriction and supplementation of one-carbon (1C) metabolites can impact offspring growth and DNA methylation. However, longitudinal research of 1C metabolite and amino acid (AA) concentrations over the reproductive cycle of human pregnancy is limited.

OBJECTIVE

To investigate longitudinal 1C metabolite and AA concentrations prior to and during pregnancy and the effects of a small-quantity lipid-based nutrition supplement (LNS) containing >20 micronutrients and prepregnancy BMI (ppBMI).

METHODS

This study was an ancillary study of the Women First Trial (NCT01883193, clinicaltrials.gov) focused on a subset of Guatemalan women (n = 134), 49% of whom entered pregnancy with a BMI ≥25 kg/m2. Ninety-five women received LNS during pregnancy (+LNS group), while the remainder did not (-LNS group). A subset of women from the Pakistan study site (n = 179) were used as a replication cohort, 124 of whom received LNS. Maternal blood was longitudinally collected on dried blood spot (DBS) cards at preconception, and at 12 and 34 wk gestation. A targeted metabolomics assay was performed on DBS samples at each time point using LC-MS/MS. Longitudinal analyses were performed using linear mixed modeling to investigate the influence of time, LNS, and ppBMI.

RESULTS

Concentrations of 23 of 27 metabolites, including betaine, choline, and serine, changed from preconception across gestation after application of a Bonferroni multiple testing correction (P < 0.00185). Sixteen of those metabolites showed similar changes in the replication cohort. Asymmetric and symmetric dimethylarginine were decreased by LNS in the participants from Guatemala. Only tyrosine was statistically associated with ppBMI at both study sites.

CONCLUSIONS

Time influenced most 1C metabolite and AA concentrations with a high degree of similarity between the 2 diverse study populations. These patterns were not significantly altered by LNS consumption or ppBMI. Future investigations will focus on 1C metabolite changes associated with infant outcomes, including DNA methylation. This trial was registered at clinicaltrials.gov as NCT01883193.

Trimethylamine N-Oxide Metabolites in Early Pregnancy and Risk of Gestational Diabetes: A Nested Case-Control Study

OBJECTIVES

This study aimed to investigate the associations between trimethylamine N-oxide (TMAO) and related metabolites in early pregnancy and the risk of gestational diabetes mellitus (GDM).

DESIGN

A prospective cohort of 22,302 pregnant women from 2010 to 2012 in Tianjin, China, was used to perform a nested case-control study. A total of 243 women with GDM and 243 women without GDM matched by maternal age (±1 year) were used as cases and controls, respectively. Conditional logistic regression and restricted cubic spline were used to examine the full-range risk associations between individual TMAOs metabolites at the first antenatal care visit with GDM. Trimethylamine conversion ratio (TMAR) was defined as trimethylamine (TMA)/its precursors, and trimethylamine N-oxide conversion ratio (TMAOR) was defined as TMAO/TMA. An additive interaction between high TMAR and low TMAOR indicates a state of TMA accumulation, and a mathematical interaction between high TMAR and high TMAOR indicates accumulation of TMAO.

RESULTS

TMA was linearly associated with GDM, whereas TMA precursors and TMAO were inversely associated with GDM with clear threshold effects, i.e., 16 nmol/mL for TMAO, 200 nmol/mL for betaine, 112 nmol/mL for l-carnitine, and 110 and 270 nmol/mL for cholinechloride (a U-shaped relationship). Copresence of TMAR >0.35 and TMAOR ≤0.15 was associated with a markedly higher OR (11.16; 95% CI, 5.45 to 22.8), compared with TMAR >0.35 only (OR = 1.71; 95% CI, 0.42 to 6.95) or TMAOR ≤0.15 only (OR = 2.06; 95% CI, 1.09 to 3.90), with a significant additive interaction. However, the mathematical interaction was nonsignificant.

CONCLUSIONS

TMAO metabolites in the early pregnancy were associated with the risk of GDM, whereas TMA was more likely to play a causal role in GDM.

Differences in Pregnancy Metabolic Profiles and Their Determinants between White European and South Asian Women: Findings from the Born in Bradford Cohort

There is widespread metabolic disruption in women upon becoming pregnant. South Asians (SA) compared to White Europeans (WE) have more fat mass and are more insulin-resistant at a given body mass index (BMI). Whether these are reflected in other gestational metabolomic differences is unclear. Our aim was to compare gestational metabolic profiles and their determinants between WE and SA women. We used data from a United Kingdom (UK) cohort to compare metabolic profiles and associations of maternal age, education, parity, height, BMI, tricep skinfold thickness, gestational diabetes (GD), pre-eclampsia, and gestational hypertension with 156 metabolic measurements in WE (n = 4072) and SA (n = 4702) women. Metabolic profiles, measured in fasting serum taken between 26–28 weeks gestation, were quantified by nuclear magnetic resonance. Distributions of most metabolic measures differed by ethnicity. WE women had higher levels of most lipoprotein subclasses, cholesterol, glycerides and phospholipids, monosaturated fatty acids, and creatinine but lower levels of glucose, linoleic acid, omega-6 and polyunsaturated fatty acids, and most amino acids. Higher BMI and having GD were associated with higher levels of several lipoprotein subclasses, triglycerides, and other metabolites, mostly with stronger associations in WEs. We have shown differences in gestational metabolic profiles between WE and SA women and demonstrated that associations of exposures with these metabolites differ by ethnicity.

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.

Metabolomics in genetic testing

Metabolomics is an intriguing field of study providing a new readout of the biochemical activities taking place at the moment of sampling within a subject's biofluid or tissue. Metabolite concentrations are influenced by several factors including disease, environment, drugs, diet and, importantly, genetics. Metabolomics signatures, which describe a subject's phenotype, are useful for disease diagnosis and prognosis, as well as for predicting and monitoring the effectiveness of treatments. Metabolomics is conventionally divided into targeted (i.e., the quantitative analysis of a predetermined group of metabolites) and untargeted studies (i.e., analysis of the complete set of small-molecule metabolites contained in a biofluid without a pre-imposed metabolites-selection). Both approaches have demonstrated high value in the investigation and understanding of several monogenic and multigenic conditions. Due to low costs per sample and relatively short analysis times, metabolomics can be a useful and robust complement to genetic sequencing.

Metabolomics applied to maternal and perinatal health: a review of new frontiers with a translation potential

The prediction or early diagnosis of maternal complications is challenging mostly because the main conditions, such as preeclampsia, preterm birth, fetal growth restriction, and gestational diabetes mellitus, are complex syndromes with multiple underlying mechanisms related to their occurrence. Limited advances in maternal and perinatal health in recent decades with respect to preventing these disorders have led to new approaches, and "omics" sciences have emerged as a potential field to be explored. Metabolomics is the study of a set of metabolites in a given sample and can represent the metabolic functioning of a cell, tissue or organism. Metabolomics has some advantages over genomics, transcriptomics, and proteomics, as metabolites are the final result of the interactions of genes, RNAs and proteins. Considering the recent "boom" in metabolomic studies and their importance in the research agenda, we here review the topic, explaining the rationale and theory of the metabolomic approach in different areas of maternal and perinatal health research for clinical practitioners. We also demonstrate the main exploratory studies of these maternal complications, commenting on their promising findings. The potential translational application of metabolomic studies, especially for the identification of predictive biomarkers, is supported by the current findings, although they require external validation in larger datasets and with alternative methodologies.