GC-MS based Gestational Diabetes Mellitus longitudinal study: Identification of 2-and 3-hydroxybutyrate as potential prognostic biomarkers

Urine Metabolomics Reveals Overlapping Metabolic Associations Between Preeclampsia and Gestational Diabetes

Pregnancy is associated with numerous metabolic adaptations to meet the demands of the growing foetus. These adaptations could be perturbed during pregnancy due to preeclampsia (PE) and gestational diabetes (GDM). As these two obstetric aliments show some overlapping pathophysiology and similar biochemical dysregulation, the present study was undertaken to compare urine metabolome of PE and GDM with normal pregnancy (NT) in all trimesters of gestation using nuclear magnetic resonance spectroscopy-based metabolomics analysis to ascertain and compare metabolome in the study groups. We observed overlapping metabolic perturbations in PE and GDM. Though a study with a small sample size, this is the first report which confirms significantly differential metabolites in urine of both PE and GDM. Dimethylglycine and oxoglutaric acid were decreased while benzoic acid was increased in both the cases in all trimesters. Alanine, aspartate and glutamate metabolism, aminoacyl-tRNA biosynthesis, citrate and butanoate metabolism were the most perturbed pathways in both PE and GDM across pregnancy. These pathways have an association with energy metabolism, glucose homeostasis, insulin sensitivity and oxidative stress which play an important role in the development and progression of PE and GDM. In conclusion, our study showed that urine metabolome could reflect metabolic associations between PE and GDM and also in the identification of biomolecules that could be used as potential biomarker(s) for early detection of the metabolic diseases in pregnancy.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12291-022-01103-2.

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

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

Enzymatic detection of α-hydroxybutyrate, an important marker of insulin resistance, and comparison with LC-MS/MS detection

Aim

The metabolite α-hydroxybutyrate (α-HB) is an important marker of insulin resistance and impaired glucose tolerance allowing to identify patients at risk of developing diabetes and related metabolic disorders before any symptoms become apparent. At present, its exact quantification requires mass spectrometry (LC-MS), which is not compatible with routine laboratory use. Accordingly, a simple enzymatic-based method was assessed and its applicability and measuring accuracy compared with LC-MS was investigated.

Methods

Standards, serum, and plasma samples containing α-HB were prepared with routine procedures and their α-HB contents measured with the XpressGT® enzymatic test kit photometrically or with LC-MS and multiple reaction monitoring.

Results

α-HB detection with XpressGT® yielded highly linear calibration curves and 102 % recovery of stocks added to commercial samples. Stability of the analyte in serum and plasma samples prepared with various anti-coagulants was >90 % after 46 h for several widely used preparations and recovery after 3 freeze-thaw cycles was ≥95 % with these anti-coagulants. A direct comparison of 75 samples indicated very good agreement of α-HB levels determined by both methods, 86 % of XpressGT® samples being within ±20 % of LC-MS values and even 93 % within ±20 % considering only samples above 30 μM concentration.

Conclusion

XpressGT®-based detection of α-HB is an easily applicable method which can be used for accurate and reliable quantification of the metabolite in clinical practice. Routine α-HB determination in patients at risk of developing diabetes would allow early establishment of preventive measures or pharmacological intervention reducing the risk for the onset of serious diabetes-related health problems.

Neurodevelopment and Metabolism in the Maternal-Placental-Fetal Unit

Importance

Disturbances in maternal, placental, and fetal metabolism are associated with developmental outcomes. Associations of maternal, placental, and fetal metabolism with subsequent neurodevelopmental outcomes in the child are understudied.

Objective

To investigate the metabolic associations within the maternal-placental-fetal unit and subsequent neurodevelopmental outcomes in younger siblings of children with autism spectrum disorder (ASD).

Design, Setting, and Participants

This cohort study was conducted within a subset of the Markers of Autism Risk in Babies, Learning Early Signs (MARBLES) cohort. MARBLES is a prospective birth cohort of younger siblings of children with ASD assessed for neurodevelopmental outcomes at approximately age 36 months. Participants in MARBLES were recruited through the UC Davis MIND Institute. This subset of the MARBLES cohort included younger siblings born between 2009 and 2015. Maternal third trimester serum, placental tissue, and umbilical cord serum samples were collected from participants. Only pregnancies with at least 2 of these sample types were included in this analysis. Data analysis was conducted from March 1, 2023, to March 15, 2024.

Exposures

Quantitative metabolomics analysis was conducted on maternal third trimester serum, as well as placental tissue and umbilical cord serum collected at delivery.

Main Outcomes and Measures

Using the Autism Diagnostic Observation Schedule and Mullen Scales of Early Learning, outcomes were classified as ASD, other nontypical development (non-TD), and typical development (TD).

Results

This analysis included 100 maternal serum samples, 141 placental samples, and 124 umbilical cord serum samples from 152 pregnancies (median [IQR] maternal age, 34.6 [30.8-38.3] years; median [IQR] gestational age, 39.0 [38.6-39.7] weeks; 87 [57.2%] male infants). There was no evidence that the maternal third trimester serum metabolome was significantly associated with the other metabolomes. The placental and cord serum metabolomes were highly correlated (first latent variate pair: R2 = 0.75; P < .001) and the variate scores for each tissue were significantly associated with reduced risk of non-TD (placenta: relative risk [RR], 0.13; 95% CI, 0.02-0.71; cord: RR, 0.13; 95% CI, 0.03-0.70) but not ASD (placenta: RR, 1.09; 95% CI, 0.42-2.81; cord: RR, 0.63; 95% CI, 0.23-1.73) compared with the TD reference group.

Conclusions and Relevance

In this cohort study of children with high familial risk of ASD, placental and cord serum metabolism at delivery were highly correlated. Furthermore, placental and cord serum metabolic profiles were associated with risk of non-TD.

Association of Placental Tissue Metabolite Levels with Gestational Diabetes Mellitus: a Metabolomics Study

The purpose of the study is to investigate the metabolic characteristics of placental tissue in patients diagnosed with gestational diabetes mellitus (GDM). Ultra-performance liquid chromatography-mass spectrometry (UPLC-MS/MS) was employed to qualitatively and quantitatively analyze the metabolites in placental tissues obtained from 25 healthy pregnant women and 25 pregnant women diagnosed with GDM. Multilevel statistical methods are applied to process intricate metabolomics data. Meanwhile, we applied machine learning techniques to identify biomarkers that could potentially predict the risk of long-term complications in patients with GDM as well as their offspring. We identified 1902 annotated metabolites, out of which 212 metabolites exhibited significant differences in GDM placentas. In addition, the study identifies a set of risk biomarkers that effectively predict the likelihood of long-term complications in both pregnant women with GDM and their offspring. The accuracy of this panel was measured by the area under the receiver operating characteristic curve (ROC), which was found to be 0.992 and 0.960 in the training and validation sets, respectively. This study enhances our understanding of GDM pathogenesis through metabolomics. Furthermore, the panel of risk markers identified could prove to be a valuable tool in predicting potential long-term complications for both GDM patients and their offspring.

Recent progress in metabolic reprogramming in gestational diabetes mellitus: a review

Gestational diabetes mellitus is a prevalent metabolic disease that can impact the normal course of pregnancy and delivery, leading to adverse outcomes for both mother and child. Its pathogenesis is complex and involves various factors, such as insulin resistance and β-cell dysfunction. Metabolic reprogramming, which involves mitochondrial oxidative phosphorylation and glycolysis, is crucial for maintaining human metabolic balance and is involved in the pathogenesis and progression of gestational diabetes mellitus. However, research on the link and metabolic pathways between metabolic reprogramming and gestational diabetes mellitus is limited. Therefore, we reviewed the relationship between metabolic reprogramming and gestational diabetes mellitus to provide new therapeutic strategies for maternal health during pregnancy and reduce the risk of developing gestational diabetes mellitus.

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

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

Metabolomic Profiles of Nonobese and Obese Women With Gestational Diabetes

CONTEXT

In non-pregnant population, nonobese individuals with obesity-related metabolome have increased risk for type 2 diabetes and cardiovascular diseases. The risk of these diseases is also increased after gestational diabetes.

OBJECTIVE

This work aimed to examine whether nonobese (body mass index [BMI] < 30) and obese (BMI ≥ 30) women with gestational diabetes mellitus (GDM) and obese non-GDM women differ in metabolomic profiles from nonobese non-GDM controls.

METHODS

Levels of 66 metabolic measures were assessed in early (median 13, IQR 12.4-13.7 gestation weeks), and across early, mid (20, 19.3-23.0), and late (28, 27.0-35.0) pregnancy blood samples in 755 pregnant women from the PREDO and RADIEL studies. The independent replication cohort comprised 490 pregnant women.

RESULTS

Nonobese and obese GDM, and obese non-GDM women differed similarly from the controls across early, mid, and late pregnancy in 13 measures, including very low-density lipoprotein-related measures, and fatty acids. In 6 measures, including fatty acid (FA) ratios, glycolysis-related measures, valine, and 3-hydroxybutyrate, the differences between obese GDM women and controls were more pronounced than the differences between nonobese GDM or obese non-GDM women and controls. In 16 measures, including HDL-related measures, FA ratios, amino acids, and inflammation, differences between obese GDM or obese non-GDM women and controls were more pronounced than the differences between nonobese GDM women and controls. Most differences were evident in early pregnancy, and in the replication cohort were more often in the same direction than would be expected by chance alone.

CONCLUSION

Differences between nonobese and obese GDM, or obese non-GDM women and controls in metabolomic profiles may allow detection of high-risk women for timely targeted preventive interventions.

Molecular pathways and nutrigenomic review of insulin resistance development in gestational diabetes mellitus

Gestational diabetes mellitus is a condition marked by raised blood sugar levels and insulin resistance that usually occurs during the second or third trimester of pregnancy. According to the World Health Organization, hyperglycemia affects 16.9% of pregnancies worldwide. Dietary changes are the primarily alternative treatment for gestational diabetes mellitus. This paper aims to perform an exhaustive overview of the interaction between diet, gene expression, and the metabolic pathways related to insulin resistance. The intake of foods rich in carbohydrates can influence the gene expression of glycolysis, as well as foods rich in fat, can disrupt the beta-oxidation and ketogenesis pathways. Furthermore, vitamins and minerals are related to inflammatory processes regulated by the TLR4/NF-κB and one carbon metabolic pathways. We indicate that diet regulated gene expression of PPARα, NOS, CREB3L3, IRS, and CPT I, altering cellular physiological mechanisms and thus increasing or decreasing the risk of gestational diabetes. The alteration of gene expression can cause inflammation, inhibition of fatty acid transport, or on the contrary help in the modulation of ketogenesis, improve insulin sensitivity, attenuate the effects of glucotoxicity, and others. Therefore, it is critical to comprehend the metabolic changes of pregnant women with gestational diabetes mellitus, to determine nutrients that help in the prevention and treatment of insulin resistance and its long-term consequences.

Prediction of pre-diabetes and type 2 diabetes nine years postpartum using serum metabolome in pregnant women with gestational diabetes requiring pharmacological treatment

AIMS

We examined the association between serum metabolome in women with pharmacologically treated gestational diabetes (GDM) and measures of glucose metabolism 9 years postpartum.

METHODS

Serum targeted metabolome, adiponectin, inflammatory markers, and insulin-like growth factor-binding protein-1 phosphoisoforms were analyzed at the time of diagnosing GDM. Glucose metabolism and insulin resistance were assessed at 9 years postpartum. Data from 119 subjects were available for analyses. Associations between baseline measures and future measures of glycemia were examined with univariate regressions and multivariate prediction models. This is a secondary analysis of a previous prospective trial (NCT02417090).

RESULTS

Baseline serum markers were most strongly related to measures of insulin resistance at 9-years follow-up. In multivariate analyses combination of IDL cholesterol, early gestational weight gain and in oral glucose tolerance test fasting and 2-h glucose predicted development of disorders of glucose metabolism (pre-diabetes and/or type 2 diabetes) better than clinical predictors alone (ROC-AUC 0.75 vs. 0.65, p = 0.020).

CONCLUSIONS

Serum metabolome in pregnancy in women with GDM is related to future glucose metabolism and insulin resistance. Compared to clinical variables alone metabolome might result in better prediction of future disorders of glucose metabolism and could facilitate personalized risk stratification for postpartum interventions and follow-up.

Gestational Diabetes Is Characterized by Decreased Medium-Chain Acylcarnitines and Elevated Purine Degradation Metabolites across Pregnancy: A Case-Control Time-Course Analysis

Gestational Diabetes Mellitus (GDM) results in complications affecting both mothers and their offspring. Metabolomic analysis across pregnancy provides an opportunity to better understand GDM pathophysiology. The objective was to conduct a metabolomics analysis of first and third trimester plasma samples to identify metabolic differences associated with GDM development. Forty pregnant women with overweight/obesity from a multisite clinical trial of a lifestyle intervention were included. Participants who developed GDM (n = 20; GDM group) were matched with those who did not develop GDM (n = 20; Non-GDM group). Plasma samples collected at the first (10-16 weeks) and third (28-35 weeks) trimesters were analyzed with ultra-performance liquid chromatography-mass spectrometry (UPLC-MS). Cardiometabolic risk markers, dietary recalls, and physical activity metrics were also assessed. Four medium-chain acylcarnitines, lauroyl-, octanoyl-, decanoyl-, and decenoylcarnitine, significantly differed over the course of pregnancy in the GDM vs Non-GDM group in a group-by-time interaction (p < 0.05). Hypoxanthine and inosine monophosphate were elevated in the GDM group (p < 0.04). In both groups over time, bile acids and sorbitol increased while numerous acylcarnitines and α-hydroxybutyrate decreased (p < 0.05). Metabolites involved in fatty acid oxidation and purine degradation were altered across the first and third trimesters of GDM-affected pregnancies, providing insight into metabolites and metabolic pathways altered with GDM development.

Identification of potential biomarkers and metabolic insights for gestational diabetes prevention: A review of evidence contrasting gestational diabetes versus weight loss studies that may direct future nutritional metabolomics studies

Gestational diabetes mellitus (GDM) significantly increases maternal health risks and adverse effects for the offspring. Observational studies suggest that weight loss before pregnancy may be a promising GDM prevention method. Still, biochemical pathways linking preconception weight changes with subsequent development of GDM among women who are overweight or obese remain unclear. Metabolomic assessment is a powerful approach for understanding the global biochemical pathways linking preconception weight changes and subsequent GDM. We hypothesize that many of the alterations of metabolite levels associated with GDM will change in one direction in GDM studies but will change in the opposite direction in studies focusing on lifestyle interventions for weight loss. The present review summarizes available evidence from 21 studies comparing women with GDM with healthy participants and 12 intervention studies that investigated metabolite changes that occurred during weight loss using caloric restriction and behavioral interventions. We discuss 15 metabolites, including amino acids, lipids, amines, carbohydrates, and carbohydrate derivatives. Of particular note are the altered levels of branched-chain amino acids, alanine, palmitoleic acid, lysophosphatidylcholine 18:1, and hypoxanthine because of their mechanistic links to insulin resistance and weight change. Mechanisms that may explain how these metabolite modifications contribute to GDM development in those who are overweight or obese are proposed, including insulin resistance pathways. Future nutritional metabolomics preconception intervention studies in overweight or obese are necessary to investigate whether weight loss through lifestyle intervention can reduce GDM occurrence in association with these metabolite alterations and to test the value of these metabolites as potential diagnostic biomarkers of GDM development.

Targeted and untargeted metabolomic approach for GDM diagnosis

Gestational diabetes mellitus (GDM) is a disorder which manifests itself for the first time during pregnancy and is mainly connected with glucose metabolism. It is also known that fatty acid profile changes in erythrocyte membranes and plasma could be associated with obesity and insulin resistance. These factors can lead to the development of diabetes. In the reported study, we applied the untargeted analysis of plasma in GDM against standard glucose-tolerant (NGT) women to identify the differences in metabolomic profiles between those groups. We found higher levels of 2-hydroxybutyric and 3-hydroxybutyric acids. Both secondary metabolites are associated with impaired glucose metabolism. However, they are products of different metabolic pathways. Additionally, we applied lipidomic profiling using gas chromatography to examine the fatty acid composition of cholesteryl esters in the plasma of GDM patients. Among the 14 measured fatty acids characterizing the representative plasma lipidomic cluster, myristic, oleic, arachidonic, and α-linoleic acids revealed statistically significant changes. Concentrations of both myristic acid, one of the saturated fatty acids (SFAs), and oleic acid, which belong to monounsaturated fatty acids (MUFAs), tend to decrease in GDM patients. In the case of polyunsaturated fatty acids (PUFAs), some of them tend to increase (e.g., arachidonic), and some of them tend to decrease (e.g., α-linolenic). Based on our results, we postulate the importance of hydroxybutyric acid derivatives, cholesteryl ester composition, and the oleic acid diminution in the pathophysiology of GDM. There are some evidence suggests that the oleic acid can have the protective role in diabetes onset. However, metabolic alterations that lead to the onset of GDM are complex; therefore, further studies are needed to confirm our observations.

A Study of the Metabolic Pathways Affected by Gestational Diabetes Mellitus: Comparison with Type 2 Diabetes

BACKGROUND

Gestational diabetes mellitus (GDM) remains incompletely understood and increases the risk of developing Diabetes mellitus type 2 (DM2). Metabolomics provides insights etiology and pathogenesis of disease and discovery biomarkers for accurate detection. Nuclear magnetic resonance (NMR) spectroscopy is a key platform defining metabolic signatures in intact serum/plasma. In the present study, we used NMR-based analysis of macromolecules free-serum to accurately characterize the altered metabolic pathways of GDM and assessing their similarities to DM2. Our findings could contribute to the understanding of the pathophysiology of GDM and help in the identification of metabolomic markers of the disease.

METHODS

Sixty-two women with GDM matched with seventy-seven women without GDM (control group). ¹H NMR serum spectra were acquired on an 11.7 T Bruker Avance DRX NMR spectrometer.

RESULTS

We identified 55 metabolites in both groups, 25 of which were significantly altered in the GDM group. GDM group showed elevated levels of ketone bodies, 2-hydroxybutyrate and of some metabolic intermediates of branched-chain amino acids (BCAAs) and significantly lower levels of metabolites of one-carbon metabolism, energy production, purine metabolism, certain amino acids, 3-methyl-2-oxovalerate, ornithine, 2-aminobutyrate, taurine and trimethylamine N-oxide.

CONCLUSION

Metabolic pathways affected in GDM were beta-oxidation, ketone bodies metabolism, one-carbon metabolism, arginine and ornithine metabolism likewise in DM2, whereas BCAAs catabolism and aromatic amino acids metabolism were affected, but otherwise than in DM2.

Molecular biomarkers for gestational diabetes mellitus and postpartum diabetes

ABSTRACT

Gestational diabetes mellitus (GDM) is a growing public health problem worldwide that threatens both maternal and fetal health. Identifying individuals at high risk for GDM and diabetes after GDM is particularly useful for early intervention and prevention of disease progression. In the last decades, a number of studies have used metabolomics, genomics, and proteomic approaches to investigate associations between biomolecules and GDM progression. These studies clearly demonstrate that various biomarkers reflect pathological changes in GDM. The established markers have potential use as screening and diagnostic tools in GDM and in postpartum diabetes research. In the present review, we summarize recent studies of metabolites, single-nucleotide polymorphisms, microRNAs, and proteins associated with GDM and its transition to postpartum diabetes, with a focus on their predictive value in screening and diagnosis.

Serum metabolomic signatures of gestational diabetes in South Asian and white European women

INTRODUCTION

This study aimed to identify serum metabolomic signatures associated with gestational diabetes mellitus (GDM), and to examine if ethnic-specific differences exist between South Asian and white European women.

RESEARCH DESIGN AND METHODS

Prospective cohort study with a nested case-control analysis of 600 pregnant women from two Canadian birth cohorts; using an untargeted approach, 63 fasting serum metabolites were measured and analyzed using multisegment injection-capillary electrophoresis-mass spectrometry. Multivariate logistic regression modeling was conducted overall and by cohort.

RESULTS

The proportion of women with GDM was higher in South Asians (27.1%) compared with white Europeans (17.9%). Several amino acid, carbohydrate, and lipid pathways related to GDM were common to South Asian and white European women. Elevated circulating concentrations of glutamic acid, propionylcarnitine, tryptophan, arginine, 2-hydroxybutyric acid, 3-hydroxybutyric acid, and 3-methyl-2-oxovaleric acid were associated with higher odds of GDM, while higher glutamine, ornithine, oxoproline, cystine, glycine with lower odds of GDM. Per SD increase in glucose concentration, the odds of GDM increased (OR=2.07, 95% CI 1.58 to 2.71), similarly for metabolite ratios: glucose to glutamine (OR=2.15, 95% CI 1.65 to 2.80), glucose to creatinine (OR=1.79, 95% CI 1.39 to 2.32), and glutamic acid to glutamine (OR=1.46, 95% CI 1.16 to 1.83). South Asians had higher circulating ratios of glucose to glutamine, glucose to creatinine, arginine to ornithine, and citrulline to ornithine, compared with white Europeans.

CONCLUSIONS

We identified a panel of serum metabolites implicated in GDM pathophysiology, consistent in South Asian and white European women. The metabolic alterations leading to larger ratios of glucose to glutamine, glucose to creatinine, arginine to ornithine, and citrulline to ornithine in South Asians likely reflect the greater burden of GDM among South Asians compared with white Europeans.

Systemic Lactate Acts as a Metabolic Buffer in Humans and Prevents Nutrient Overflow in the Postprandial Phase

On an organismal level, metabolism needs to react in a well-orchestrated manner to metabolic challenges such as nutrient uptake. Key metabolic hubs in human blood are pyruvate and lactate, both of which are constantly interconverted by very fast exchange fluxes. The quantitative contribution of different food sources to these metabolite pools remains unclear. Here, we applied in vivo stable isotope labeling to determine postprandial metabolic fluxes in response to two carbohydrate sources of different complexity. Depending on the ingested carbohydrate source, glucose or wheat flour, the net direction of the lactate dehydrogenase, and the alanine amino transferase fluxes were adjusted in a way to ensure sufficient availability, while, at the same time, preventing an overflow in the respective metabolite pools. The systemic lactate pool acts as a metabolic buffer which is fueled in the early- and depleted in the late-postprandial phase and thus plays a key role for systemic metabolic homeostasis.

Specific gut bacterial and fungal microbiota pattern in the first half of pregnancy is linked to the development of gestational diabetes mellitus in the cohort including obese women

AIMS

Gestation is linked to changes in gut microbiota composition and function. Since gestational diabetes mellitus (GDM) can develop at any time of the pregnancy, we stratified the women into four groups according to the time and test used for the diagnosis. We focused on the gut microbiota pattern in early pregnancy to detect changes which could be linked to later GDM development.

METHODS

We collected stool samples from 104 pregnant women including obese individuals (first trimester body mass index median was 26.73). We divided the women into four groups according to routine screening of fasting plasma glucose (FPG) levels and oral glucose tolerance test (oGTT) in the first and third trimesters, respectively. We processed the stool samples for bacterial 16S rRNA and fungal ITS1 genes sequencing by Illumina MiSeq approach and correlated the gut microbiota composition with plasma short-chain fatty acid levels (SCFA).

RESULTS

We found that gut bacterial microbiota in the first trimester significantly differs among groups with different GDM onset based on unweighted UniFrac distances (p=0.003). Normoglycemic women had gut microbiota associated with higher abundance of family Prevotellaceae, and order Fusobacteriales, and genus Sutterella. Women diagnosed later during pregnancy either by FGP levels or by oGTT had higher abundances of genera Enterococcus, or Erysipelotrichaceae UCG-003, respectively. We observed significant enrichment of fungal genus Mucor in healthy pregnant women whereas Candida was more abundant in the group of pregnant women with impaired oGTT. Using correlation analysis, we found that Holdemanella negatively correlated with Blautia and Candida abundances and that Escherichia/Shigella abundance positively correlated and Subdoligranulum negatively correlated with plasma lipid levels. Coprococcus, Akkermansia, Methanobrevibacter, Phascolarctobacterium and Alistipes positively correlated with acetate, valerate, 2-hydroxybutyrate and 2-methylbutyrate levels, respectively, in women with GDM.

CONCLUSIONS

We conclude that there are significant differences in the gut microbiota composition between pregnant women with and without GDM already at the early stage of pregnancy in our cohort that included also overweight and obese individuals. Specific microbial pattern associated with GDM development during early pregnancy and its correlation to plasma lipid or SCFA levels could help to identify women in higher risk of GDM development.

Lead exposure and serum metabolite profiles in pregnant women in Mexico City

BACKGROUND

Lead (Pb) exposure is a global health hazard causing a wide range of adverse health outcomes. Yet, the mechanisms of Pb toxicology remain incompletely understood, especially during pregnancy. To uncover biological pathways impacted by Pb exposure, this study investigated serum metabolomic profiles during the third trimester of pregnancy that are associated with blood Pb and bone Pb.

METHODS

We used data and specimens from 99 women enrolled in the Programming Research in Obesity, Growth, Environment, and Social Stressors birth cohort in Mexico City. Maternal Pb exposure was measured in whole blood samples from the third trimester of pregnancy and in the tibia and patella bones at 1 month postpartum. Third-trimester serum samples underwent metabolomic analysis; metabolites were identified based on matching to an in-house analytical standard library. A metabolome-wide association study was performed using multiple linear regression models. Class- and pathway-based enrichment analyses were also conducted.

RESULTS

The median (interquartile range) blood Pb concentration was 2.9 (2.6) µg/dL. Median bone Pb, measured in the tibia and patella, were 2.5 (7.3) µg/g and 3.6 (9.5) µg/g, respectively. Of 215 total metabolites identified in serum, 31 were associated with blood Pb (p < 0.05). Class enrichment analysis identified significant overrepresentation of metabolites classified as fatty acids and conjugates, amino acids and peptides, and purines. Tibia and patella Pb were associated with 14 and 8 metabolites, respectively (p < 0.05). Comparing results from bone and blood Pb, glycochenodeoxycholic acid, glycocholic acid, and 1-arachidonoylglycerol were positively associated with blood Pb and tibia Pb, and 7-methylguanine was negatively associated with blood Pb and patella Pb. One metabolite, 5-aminopentanoic acid, was negatively associated with all three Pb measures.

CONCLUSIONS

This study identified serum metabolites in pregnant women associated with Pb measured in blood and bone. These findings provide insights on the metabolic profile around Pb exposure in pregnancy and information to guide mechanistic studies of toxicological effects for mothers and children.

Gas Chromatography-Mass Spectroscopy-Based Metabolomics Analysis Reveals Potential Biochemical Markers for Diagnosis of Gestational Diabetes Mellitus

Due to many adverse effects of gestational diabetes mellitus (GDM) on the mother and fetus, its diagnosis is crucial. The presence of GDM can be confirmed by an abnormal fasting plasma glucose level (aFPG) and/or oral glucose tolerance test (OGTT) performed mostly between 24 and 28 gestational week. Both aFPG and abnormal glucose tolerance (aGT) are used to diagnose GDM. In comparison to measurement of FPG, OGTT is time-consuming, usually inconvenient for the patient, and very often needs to be repeated. Therefore, it is necessary to seek tests that will be helpful and convenient to diagnose GDM. For this reason, we investigated the differences in fasting serum metabolites between GDM women with abnGM and normal FPG (aGT-GDM group), with aFPG and normal glucose metabolism (aFPG-GDM group) as well as pregnant women with normal glucose tolerance (NGT) being a control group. Serum metabolites were measured by an untargeted approach using gas chromatography–mass spectrometry (GC–MS). In the discovery phase, fasting serum samples collected from 79 pregnant women (aFPG-GDM, n = 24; aGT-GDM, n = 26; NGT, n = 29) between 24 and 28 weeks of gestation (gwk) were fingerprinted. A set of metabolites (α–hydroxybutyric acid (α–HB), β–hydroxybutyric acid (β–HB), and several fatty acids) significant in aGT-GDM vs NGT but not significant in aFPG-GDM vs NGT comparison in the discovery phase was selected for validation. These metabolites were quantified by a targeted GC–MS method in a validation cohort consisted of 163 pregnant women (aFPG-GDM, n = 51; aGT-GDM, n = 44; and NGT, n = 68). Targeted analyses were also performed on the serum collected from 92 healthy women in the first trimester (8–14 gwk) who were NGT at this time, but in the second trimester (24–28 gwk) they were diagnosed with GDM. It was found that α–HB, β–HB, and several fatty acids were associated with aGT-GDM. A combination of α–HB, β–HB, and myristic acid was found highly specific and sensitive for the diagnosis of GDM manifested by aGT-GDM (AUC = 0.828) or to select women at a risk of aGT-GDM in the first trimester (AUC = 0.791). Our findings provide new potential markers of GDM and may have implications for its early diagnosis.

Metabolites involved in purine degradation, insulin resistance, and fatty acid oxidation are associated with prediction of Gestational diabetes in plasma

INTRODUCTION

Gestational diabetes mellitus (GDM) significantly increases maternal and fetal health risks, but factors predictive of GDM are poorly understood.

OBJECTIVES

Plasma metabolomics analyses were conducted in early pregnancy to identify potential metabolites associated with prediction of GDM.

METHODS

Sixty-eight pregnant women with overweight/obesity from a clinical trial of a lifestyle intervention were included. Participants who developed GDM (n = 34; GDM group) were matched on treatment group, age, body mass index, and ethnicity with those who did not develop GDM (n = 34; Non-GDM group). Blood draws were completed early in pregnancy (10-16 weeks). Plasma samples were analyzed by UPLC-MS using three metabolomics assays.

RESULTS

One hundred thirty moieties were identified. Thirteen metabolites including pyrimidine/purine derivatives involved in uric acid metabolism, carboxylic acids, fatty acylcarnitines, and sphingomyelins (SM) were different when comparing the GDM vs. the Non-GDM groups (p < 0.05). The most significant differences were elevations in the metabolites' hypoxanthine, xanthine and alpha-hydroxybutyrate (p < 0.002, adjusted p < 0.02) in GDM patients. A panel consisting of four metabolites: SM 14:0, hypoxanthine, alpha-hydroxybutyrate, and xanthine presented the highest diagnostic accuracy with an AUC = 0.833 (95% CI: 0.572686-0.893946), classifying as a "very good panel".

CONCLUSION

Plasma metabolites mainly involved in purine degradation, insulin resistance, and fatty acid oxidation, were altered in early pregnancy in connection with subsequent GDM development.

Placental metabolic profiling in gestational diabetes mellitus: An important role of fatty acids

Aim

Gestational diabetes mellitus (GDM) is the most common metabolic disorder during pregnancy. Accumulating studies have reported metabolites that are significantly associated with the development of GDM. However, studies on the metabolism of placenta, the most important organ of maternal‐fetal energy and material transport, are extremely rare. This study aimed to identify and discuss the relationship between differentially expressed metabolites (DEM) and clinical parameters of the mothers and newborns.

Methods

In this study, metabolites from 63 placenta tissues (32 GDM and 31 normal controls) were assayed by ultra‐performance liquid chromatography‐high resolution mass spectrometry (UPLC‐HRMS).

Results

A total of 1297 annotated metabolites were detected, of which 87 significantly different in GDM placenta. Lipids and lipid‐like molecules accounted for 62.1% of DEM as they were significantly enriched via the “biosynthesis of unsaturated fatty acids” and “fatty acid biosynthesis” pathways. Linoleic acid and α‐linolenic acid appeared to be good biomarkers for the prediction and diagnosis of GDM. In addition, the level of PC(14:0/18:0) was negatively correlated with neonatal weight. 14 metabolites significantly different in male and female offspring, with the most increase in female newborns.

Conclusion

Even if maternal blood glucose level is well controlled, there are still metabolic abnormalities in GDM. Lipids and lipid‐like molecules were the main differential metabolites, especially unsaturated fatty acids.

Maternal Metabolites Associated With Gestational Diabetes Mellitus and a Postpartum Disorder of Glucose Metabolism

CONTEXT

Gestational diabetes is associated with a long-term risk of developing a disorder of glucose metabolism. However, neither the metabolic changes characteristic of gestational diabetes in a large, multi-ancestry cohort nor the ability of metabolic changes during pregnancy, beyond glucose levels, to identify women at high risk for progression to a disorder of glucose metabolism has been examined.

OBJECTIVE

This work aims to identify circulating metabolites present at approximately 28 weeks' gestation associated with gestational diabetes mellitus (GDM) and development of a disorder of glucose metabolism 10 to 14 years later.

METHODS

Conventional clinical and targeted metabolomics analyses were performed on fasting and 1-hour serum samples following a 75-g glucose load at approximately 28 weeks' gestation from 2290 women who participated in the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) Study. Postpartum metabolic traits included fasting and 2-hour plasma glucose following a 75-g glucose load, insulin resistance estimated by the homeostasis model assessment of insulin resistance, and disorders of glucose metabolism (prediabetes and type 2 diabetes) during the HAPO Follow-Up Study.

RESULTS

Per-metabolite analyses identified numerous metabolites, ranging from amino acids and carbohydrates to fatty acids and lipids, before and 1-hour after a glucose load that were associated with GDM as well as development of a disorder of glucose metabolism and metabolic traits 10 to 14 years post partum. A core group of fasting and 1-hour metabolites mediated, in part, the relationship between GDM and postpartum disorders of glucose metabolism, with the fasting and 1-hour metabolites accounting for 15.7% (7.1%-30.8%) and 35.4% (14.3%-101.0%) of the total effect size, respectively. For prediction of a postpartum disorder of glucose metabolism, the addition of circulating fasting or 1-hour metabolites at approximately 28 weeks' gestation showed little improvement in prediction performance compared to clinical factors alone.

CONCLUSION

The results demonstrate an association of multiple metabolites with GDM and postpartum metabolic traits and begin to define the underlying pathophysiology of the transition from GDM to a postpartum disorder of glucose metabolism.

Plasma Short-Chain Fatty Acids and Their Derivatives in Women with Gestational Diabetes Mellitus

Gestational diabetes mellitus (GDM) represents a heterogeneous group of hyperglycemic metabolic disorders that are associated with health outcomes for mothers and offspring. Currently, diagnosis of GDM is based on repetitive measurement of increased fasting plasma glucose (FPG) or upon results showing increased postprandial plasma glucose (PPG). Recently, it was discovered that the changes in the gut microbiome during pregnancy are associated with insulin resistance and obesity. Therefore, in this study, relevant products of gut bacteria, short-chain fatty acids (SCFA) and their derivatives were evaluated together with baseline body composition characteristics and common biochemical parameters in women with three different phenotypes of GDM, healthy pregnant and nonpregnant women. Plasma SCFA and their derivatives were derivatized, separated on reversed-phase liquid chromatography and detected by a triple-quadrupole mass spectrometer. 3-hydroxybutyrate (3-OH-BA), 4-methylvalerate (4-MVA) and isovalerate (IVA), together with selected parameters associated with baseline body composition characteristics and biochemistry, were evaluated as statistically significant. 3-OH-BA, which was increased in all three groups of women with different phenotypes of GDM, reflects a ketogenic state of GDM. In all groups of pregnant women, elevated/suppressed concentrations of 4-MVA/IVA were found. These findings show the importance of monitoring SCFA and other parameters besides glucose in women with GDM.

Postpartum circulating microRNA enhances prediction of future type 2 diabetes in women with previous gestational diabetes

AIMS/HYPOTHESIS

Type 2 diabetes mellitus is a major cause of morbidity and death worldwide. Women with gestational diabetes mellitus (GDM) have greater than a sevenfold higher risk of developing type 2 diabetes in later life. Accurate methods for postpartum type 2 diabetes risk stratification are lacking. Circulating microRNAs (miRNAs) are well recognised as biomarkers/mediators of metabolic disease. We aimed to determine whether postpartum circulating miRNAs can predict the development of type 2 diabetes in women with previous GDM.

METHODS

In an observational study, plasma samples were collected at 12 weeks postpartum from 103 women following GDM pregnancy. Utilising a discovery approach, we measured 754 miRNAs in plasma from type 2 diabetes non-progressors (n = 11) and type 2 diabetes progressors (n = 10) using TaqMan-based real-time PCR on an OpenArray platform. Machine learning algorithms involving penalised logistic regression followed by bootstrapping were implemented.

RESULTS

Fifteen miRNAs were selected based on their importance in discriminating type 2 diabetes progressors from non-progressors in our discovery cohort. The levels of miRNA miR-369-3p remained significantly different (p < 0.05) between progressors and non-progressors in the validation sample set (n = 82; 71 non-progressors, 11 progressors) after adjusting for age and correcting for multiple comparisons. In a clinical model of prediction of type 2 diabetes that included six traditional risk factors (age, BMI, pregnancy fasting glucose, postpartum fasting glucose, cholesterol and triacylglycerols), the addition of the circulating miR-369-3p measured at 12 weeks postpartum improved the prediction of future type 2 diabetes from traditional AUC 0.83 (95% CI 0.68, 0.97) to an AUC 0.92 (95% CI 0.84, 1.00).

CONCLUSIONS

This is the first demonstration of miRNA-based type 2 diabetes prediction in women with previous GDM. Improved prediction will facilitate early lifestyle/drug intervention for type 2 diabetes prevention.

Differentiation of gestational diabetes mellitus by nuclear magnetic resonance-based metabolic plasma analysis

This study aimed to investigate the metabolic profile of gestational diabetes mellitus (GDM) at both antepartum and postpartum periods. Seventy pregnant women were divided into three groups: the normal glucose-tolerant group (NGT, n=35), the abnormal glucose-tolerant groups without insulin therapy (A1GDM, n=24) or with insulin therapy (A2GDM, n=11). Metabolic profiles of the plasma were acquired by proton nuclear magnetic resonance (¹H-NMR) spectroscopy and analyzed by multivariate statistical data analysis. The relationship between demographic parameters and the potential metabolite biomarkers was further explored. Group antepartum or postpartum showed similar metabolic trends. Compare with those of the NGT group, the levels of 2-hydroxybutyrate, lysine, acetate, glutamine, succinate, tyrosine, formate, and all three BCAAs (leucine, valine, isoleucine) in the A2GDM group were increased dramatically, and the levels of lysine, acetate, and formate in the A1GDM group were elevated significantly. The dramatically decreased levels of 3-methyl-2-oxovalerate and methanol were observed both in the A1GDM group and A2GDM group. Compare to the A1GDM group, the branched-chain amino acids (BCAAs) of leucine, valine, and isoleucine were increased dramatically in the A2GDM group. The levels of aromatic amino acids (AAAs), tyrosine and phenylalanine, were significantly increased in GDM women, consistent with the severity of GDM. Interference of amino acid metabolism and disturbance in energy metabolism occurred in women with different grades of GDM. Metabolic profiles could reflect the severity of GDM. Plasma BCAA concentrations showing strong positive correlations with weight and pre-delivery BMI. This study provides a new perspective to understand the pathogenesis and etiology of GDM, which may help the clinical management and treatment of GDM.

Discovery of Gut Bacteria Specific to Alzheimer's Associated Diseases is a Clue to Understanding Disease Etiology: Meta-Analysis of Population-Based Data on Human Gut Metagenomics and Metabolomics

Alzheimer's disease (AD)-associated sequence (ADAS) of cultured fecal bacteria was discovered in human gut targeted screening. This study provides important information to expand our current understanding of the structure/activity relationship of ADAS and putative inhibitors/activators that are potentially involved in ADAS appearance/disappearance. The NCBI database analysis revealed that ADAS presents at a large proportion in American Indian Oklahoman (C&A) with a high prevalence of obesity/diabetes and in colorectal cancer (CRC) patients from the US and China. An Oklahoman non-native group (NNI) showed no ADAS. Comparison of two large US populations reveals that ADAS is more frequent in individuals aged ≥66 and in females. Prevalence and levels of fecal metabolites are altered in the C&A and CRC groups versus controls. Biogenic amines (histamine, tryptamine, tyramine, phenylethylamine, cadaverine, putrescine, agmatine, spermidine) that present in food and are produced by gut microbiota are significantly higher in C&A (e.g., histamine/histidine 95-fold) versus NNI (histamine/histidine 16-fold). The majority of these bio-amines are cytotoxic at concentrations found in food. Inositol phosphate signaling implicated in AD is altered in C&A and CRC. Tryptamine stimulated accumulation of inositol phosphate. The seizure-eliciting tryptamine induced cytoplasmic vacuolization and vesiculation with cell fragmentation. Present additions of ADAS-carriers at different ages including infants led to an ADAS-comprising human sample size of 2,830 from 27 studies from four continents (North America, Australia, Asia, Europe). Levels of food-derived monoamine oxidase inhibitors and anti-bacterial compounds, the potential modulators of ADAS-bacteria growth and biogenic amine production, were altered in C&A versus NNI. ADAS is attributable to potentially modifiable risk factors of AD associated diseases.

Effects of Casein, Chicken, and Pork Proteins on the Regulation of Body Fat and Blood Inflammatory Factors and Metabolite Patterns Are Largely Dependent on the Protein Level and Less Attributable to the Protein Source

The impact of meat protein on metabolic regulation is still disputed and may be influenced by protein level. This study aimed to explore the effects of casein, pork, and chicken proteins at different protein levels (40% E vs 20% E) on body weight regulation, body fat accumulation, serum hormone levels, and inflammatory factors/metabolites in rats maintained on high-fat (45% E fat) diets for 84 d. Increased protein levels resulted in a significant reduction in body fat mass and an increase in the serum levels of the anti-inflammatory cytokine IL-10, independent of protein source. Analysis of blood via untargeted metabolomics analysis identified eight, four, and four metabolites significantly altered by protein level, protein source, and a protein level-source interaction, respectively. Together, the effects of casein, chicken, and pork protein on the regulation of body fat accumulation and blood metabolite profile are largely dependent on protein level and less attributable to the protein source.

Soy food intake associates with changes in the metabolome and reduced blood pressure in a gut microbiota dependent manner

BACKGROUND AND AIMS

Consumption of soy foods has been associated with protection against cardiometabolic disease, but the mechanisms are incompletely understood. We hypothesized that habitual soy food consumption associates with gut microbiome composition, metabolite production, and the interaction between diet, microbiota and metabolites.

METHODS AND RESULTS

We analyzed dietary soy intake, plasma and stool metabolites, and gut microbiome data from two independent cross-sectional samples of healthy US individuals (N = 75 lean or overweight, and N = 29 obese). Habitual soy intake associated with several circulating metabolites. There was a significant interaction between soy intake and gut microbiome composition, as defined by gut enterotype, on metabolites in plasma and stool. Soy consumption associated with reduced systolic blood pressure, but only in a subset of individuals defined by their gut microbiome enterotype, suggesting that responsiveness to soy may be dependent on microbiome composition. Soy intake was associated with differences in specific microbial taxa, including two taxa mapping to genus Dialister and Prevotella which appeared to be suppressed by high soy intake We identified context-dependent effects of these taxa, where presence of Prevotella was associated with higher blood pressure and a worse cardiometabolic profile, but only in the absence of Dialister.

CONCLUSIONS

The gut microbiome is an important intermediate in the interplay between dietary soy intake and systemic metabolism. Consumption of soy foods may shape the microbiome by suppressing specific taxa, and may protect against hypertension only in individuals with soy-responsive microbiota.

CLINICAL TRIALS REGISTRY

NCT02010359 at clinicaltrials.gov.

Plasma Metabolome Alterations Associated with Extrauterine Growth Restriction

Very preterm infants (VPI, born at or before 32 weeks of gestation) are at risk of adverse health outcomes, from which they might be partially protected with appropriate postnatal nutrition and growth. Metabolic processes or biochemical markers associated to extrauterine growth restriction (EUGR) have not been identified. We applied untargeted metabolomics to plasma samples of VPI with adequate weight for gestational age at birth and with different growth trajectories (29 well-grown, 22 EUGR) at the time of hospital discharge. A multivariate analysis showed significantly higher levels of amino-acids in well-grown patients. Other metabolites were also identified as statistically significant in the comparison between groups. Relevant differences (with corrections for multiple comparison) were found in levels of glycerophospholipids, sphingolipids and other lipids. Levels of many of the biochemical species decreased progressively as the level of growth restriction increased in severity. In conclusion, an untargeted metabolomic approach uncovered previously unknown differences in the levels of a range of plasma metabolites between well grown and EUGR infants at the time of discharge. Our findings open speculation about pathways involved in growth failure in preterm infants and the long-term relevance of this metabolic differences, as well as helping in the definition of potential biomarkers.

Integrated microbiome-metabolome analysis reveals novel associations between fecal microbiota and hyperglycemia-related changes of plasma metabolome in gestational diabetes mellitus

Gestational diabetes mellitus (GDM) has been associated with circulating metabolic disorders and alterations in gut microbiota, respectively. Although changes in gut microbiota contribute to metabolic diseases, the connections between gut microbiota and the circulating metabolic state in GDM remain largely undetermined. To investigate the associations between gut microbiota and the circulating metabolome of GDM, we enrolled 40 pregnant women (20 with GDM and 20 non-diabetic control), and performed multi-omics association (MOA) study on 16s rRNA sequencing of fecal microbiota and ¹H-NMR profiling of the plasma metabolome. The results suggested that both fecal microbiota and the plasma metabolome of the enrolled pregnant women could be separated along the vector of hyperglycemia. A close correlation between fecal microbiota and the plasma metabolome of GDM was observed by MOA approaches. Redundancy Analysis identified five plasma metabolites (glycerol, lactic acid, proline, galactitol and methylmalonic acid) and 98 members of fecal microbiota contributing to the close correlation between the plasma metabolome and fecal microbiota. Further spearman rank correlation analysis revealed that four out of five of the identified plasma metabolites (except galactitol) were correlated with hyperglycemia. Co-occurring network analysis suggested that 15 out of 98 of the members of fecal microbiota were positively correlated with each other, forming a co-occurring cohort (mainly consisted of the phylum Firmicutes). The results of this study demonstrated that alterations in fecal microbiota were associated with hyperglycemia related changes of the plasma metabolome of women with GDM, suggesting novel therapies against gut microbiota to alleviate GDM.

Metabolomics-Based Prospective Studies and Prediction of Type 2 Diabetes Mellitus Risks

The preceding decade has witnessed an intense upsurge in the diabetic population across the world making type 2 diabetes mellitus (T2DM) more of an epidemic than a lifestyle disease. Metabolic disorders are often latent for a while before becoming clinically evident, thus reinforcing the pursuit of early biomarkers of metabolic alterations. A prospective study along with metabolic profiling is the most appropriate way to detect the early pathophysiological changes in metabolic diseases such as T2DM. The aim of this review was to summarize the different potential biomarkers of T2DM identified in prospective studies, which used tools of metabolomics. The review also demonstrates on how metabolomic profiling-based prospective studies can be used to address a concern like population-specific disease mechanism. We performed a literature search on metabolomics-based prospective studies on T2DM using the key words "metabolomics," "Type 2 diabetes," "diabetes mellitus", "metabolite profiling," "prospective study," "metabolism," and "biomarker." Additional articles that were obtained from the reference lists of the articles obtained using the above key words were also examined. Articles on dietary intake, type 1 diabetes mellitus, and gestational diabetes were excluded. The review revealed that many studies showed a direct association of branched-chain amino acids and an inverse association of glycine with T2DM. Majority of the prospective studies conducted were targeted metabolomics-based, with Caucasians as their study cohort. The whole disease risk in populations, including Asians, could therefore not be identified. This review proposes the utility of prospective studies in conjunction with metabolomics platform to unravel the altered metabolic pathways that contribute to the risk of T2DM.

Metabolomics for predicting hyperglycemia in pregnancy: a protocol for a systematic review and potential meta-analysis

BACKGROUND

Hyperglycemia in pregnancy (HIP) has been recently differentiated between diabetes in pregnancy (DIP) and gestational diabetes mellitus (GDM). The proposed protocol is relevant, and clinical concern is due to the higher risk of adverse pregnancy outcomes (APO) and long-term effects on both the mother and the fetus. Fasting plasma glucose level (FPG) and oral glucose tolerance test (OGTT) are current diagnostic tools. However, controversy persists concerning diagnostic criteria, cut-off points, and even selective or universal screening. The objective of this systematic review is to assess the performance of metabolomic markers in the prediction of HIP.

METHODS

This is a protocol for a systematic review with potential meta-analysis. The primary outcome is GDM, defined as glucose intolerance identified in the second and third trimesters of pregnancy (any FPG ≥ 92 mg/dL and < 126 mg/dL OR when 75-g OGTT shows one altered value among these: FPG ≥ 92 mg/dL or 1-h post glucose load ≥ 180 mg/dL or 2-h post glucose load ≥ 153 mg/dL); the secondary outcome is HIP, defined as hyperglycemia detected in the first trimester of pregnancy (any FPG ≥ 126 mg/dL). A detailed systematic literature search will be carried out in electronic databases and conference abstracts, using the keywords "gestational diabetes mellitus," "metabolomics," "pregnancy," and "screening" (and their variations). We will include original peer-reviewed articles published from Jan 1, 1999, to Dec 31, 2018. Original studies including diabetes diagnosed before pregnancy (T2DM and T1DM), multiple pregnancies, and congenital malformations will be excluded. All results regarding samples, participant characteristics, metabolomic techniques, and diagnostic accuracy measures will be retrieved and analyzed. Since this is a systematic review, no ethical approval is necessary.

DISCUSSION

This systematic review may have the potential to provide significant evidence-based findings on the prediction performance of metabolomics. There are short and long-term repercussions for the mother and the newborn. Therefore, both may benefit from an accurate prediction technique for HIP.

SYSTEMATIC REVIEW REGISTRATION

This protocol was registered in the PROSPERO platform under number CRD42018100175 .

The metabolic fingerprints of HCV and HBV infections studied by Nuclear Magnetic Resonance Spectroscopy

Few studies are available on metabolic changes in liver injuries and this is the first metabolomic study evaluating a group of HCV-positive patients, before and after viral eradication via DAA IFN-free regimens, using ¹H-NMR to characterize and compare their serum fingerprints to naïve HBV-patients and healthy donors. The investigation clearly shows differences in the metabolomic profile of HCV patients before and after effective DAA treatment. Significant changes in metabolites levels in patients undergoing therapy suggest alterations in several metabolic pathways. It has been shown that ¹H-NMR fingerprinting approach is an optimal technique in predicting the specific infection and the healthy status of studied subjects (Monte-Carlo cross validated accuracies: 86% in the HCV vs HBV model, 98.7% in the HCV vs HC model). Metabolite data collected support the hypothesis that the HCV virus induces glycolysis over oxidative phosphorylation in a similar manner to the Warburg effect in cancer, moreover our results have demonstrated a different action of the two viruses on cellular metabolism, corroborating the hypothesis that the metabolic perturbation on patients could be attributed to a direct role in viral infection. This metabolomic study has revealed some alteration in metabolites for the first time (2-oxoglutarate and 3-hydroxybutrate) concerning the HCV-infection model that could explain several extrahepatic manifestations associated with such an infection.

Large-Scale Longitudinal Metabolomics Study Reveals Different Trimester-Specific Alterations of Metabolites in Relation to Gestational Diabetes Mellitus

Despite the increasing research attention paid to gestational diabetes mellitus (GDM) due to its high prevalence, limited knowledge is available about its pathogenesis. In this study, 428 serum samples were collected from 107 pregnant women suffering from GDM and 107 matched healthy controls. The nontargeted metabolomics data of maternal serum samples from the first (T1, n = 214) and second trimesters (T2, n = 214) were acquired by using ultrahigh performance liquid chromatography coupled with Orbitrap mass spectrometry (MS). A total of 93 differential metabolites were identified on the basis of the accurate mass and MS/MS fragmentation. After false discovery rate correction, the levels of 31 metabolites in GDM group were significantly altered in the first trimester. The differential metabolites were mainly attributed to purine metabolism, fatty acid β-oxidation, urea cycle, and tricarboxylic acid cycle pathways. The fold changes across pregnancy (T2/T1) of six amino acids (serine, proline, leucine/isoleucine, glutamic acid, tyrosine, and ornithine), a lysophosphatidylcholine (LysoPC(20:4)), and uric acid in GDM group were significantly different from those in the control groups, suggesting that these 8 metabolites might have contributed to the occurrence and progression of GDM. The findings revealed that the amino acid metabolism, lipid metabolism, and other pathways might be disturbed prior to GDM onset and during the period from the first to the second trimester of pregnancy.

Clinical and biochemical approach to predicting post-pregnancy metabolic decompensation

The prevalence of gestational diabetes in the developed world is increased and parallels that of obesity. Apart from the maternal and fetal complications occurring during pregnancy, GDM is characterized by a high subsequent risk of type 2 diabetes, metabolic syndrome, and cardiovascular disease. In this paper, we outline the different factors to consider in assessing the future risk of diabetes developing in women with a history of GDM. Looking at the modifiable risk factors, it is worth noting that promoting a healthy diet and lifestyle before (physical activity), during and after pregnancy (breast feeding) in women of fertile age are fundamental to the success of efforts to reduce the burden of diabetes in these young people.

Fasting serum α‑hydroxybutyrate and pyroglutamic acid as important metabolites for detecting isolated post-challenge diabetes based on organic acid profiles

The aim of this study was to develop a method to detect serum organic acid profiles in patients with isolated post-challenge diabetes (IPD) and to compare the metabolites between IPD patients, type 2 diabetes mellitus (T2DM) and healthy controls. We developed a gas chromatography-mass spectrometry method to detect serum organic acids and validated it using serum from 40 patients with IPD, 47 with newly diagnosed T2DM, and 48 healthy controls. We then analyzed the organic acid profiles by multivariate analysis to identify potential metabolites. This method allowed the fast and accurate measurement of 27 organic acids in serum. Serum organic acid profiles differed significantly among IPD patients, T2DM patients, and healthy controls. IPD samples had significantly higher concentrations of α‑hydroxybutyrate and β‑hydroxybutyrate (P < 0.05) and lower pyroglutamic acid concentration (P < 0.05) compared with the healthy controls, and the area under the curve for the combination of α‑hydroxybutyrate and pyroglutamic acid was 0.863 for the IPD group. These results provide useful information regarding the changes in organic acid metabolism associated with IPD. Measurement of these metabolites in fasting serum from IPD patients may provide useful diagnostic and/or prognostic biomarkers, as well as helpful markers for the therapeutic monitoring of IPD patients.

Postpartum glucose intolerance: an updated overview

The prevalence of type 2 diabetes mellitus has increased worldwide over the past three decades, as a consequence of the more westernized lifestyle, which is responsible for the increasing obesity rate in the modern adult's life. Concomitant with this increase there has been a gradual rise in the overall prevalence of gestational diabetes mellitus, a condition that strongly predisposes to overt diabetes later in life. Many women with previous gestational diabetes mellitus show glucose intolerance in the early postpartum period. Although the best screening strategy for postpartum glucose intolerance is still debated, numerous evidences indicate that identification of these women at this time is of critical importance, as efforts to initiate early intensive lifestyle modification, including hypocaloric diet and physical activity, and to ameliorate the metabolic profile of these high-risk subjects can prevent or delay the onset of type 2 diabetes mellitus. Nevertheless, less than one fifth of women attend the scheduled postpartum screening following gestational diabetes mellitus and they are at increased risk to develop type 2 diabetes mellitus later in their lives. Unsatisfying results have also come from early intervention strategies and tools that have been developed during the last few years to help improving the rate of adherence to postpartum glycemic testing, thereby indicating that more effective strategies are needed to improve women's participation in postpartum screening.

Short overview on metabolomics approach to study pathophysiology of oxidative stress in cancer

Association of oxidative stress with carcinogenesis is well known, but not understood well, as is pathophysiology of oxidative stress generated during different types of anti-cancer treatments. Moreover, recent findings indicate that cancer associated lipid peroxidation might eventually help defending adjacent nonmalignant cells from cancer invasion. Therefore, untargeted metabolomics studies designed for advanced translational and clinical studies are needed to understand the existing paradoxes in oncology, including those related to controversial usage of antioxidants aiming to prevent or treat cancer. In this short review we have tried to put emphasis on the importance of pathophysiology of oxidative stress and lipid peroxidation in cancer development in relation to metabolic adaptation of particular types of cancer allowing us to conclude that adaptation to oxidative stress is one of the main driving forces of cancer pathophysiology. With the help of metabolomics many novel findings are being achieved thus encouraging further scientific breakthroughs. Combined with targeted qualitative and quantitative methods, especially immunochemistry, further research might reveal bio-signatures of individual patients and respective malignant diseases, leading to individualized treatment approach, according to the concepts of modern integrative medicine.

Quality assurance procedures for mass spectrometry untargeted metabolomics. a review

Untargeted metabolomics, as a global approach, has already proven its great potential and capabilities for the investigation of health and disease, as well as the wide applicability for other research areas. Although great progress has been made on the feasibility of metabolomics experiments, there are still some challenges that should be faced and that includes all sources of fluctuations and bias affecting every step involved in multiplatform untargeted metabolomics studies. The identification and reduction of the main sources of unwanted variation regarding the pre-analytical, analytical and post-analytical phase of metabolomics experiments is essential to ensure high data quality. Nowadays, there is still a lack of information regarding harmonized guidelines for quality assurance as those available for targeted analysis. In this review, sources of variations to be considered and minimized along with methodologies and strategies for monitoring and improvement the quality of the results are discussed. The given information is based on evidences from different groups among our own experiences and recommendations for each stage of the metabolomics workflow. The comprehensive overview with tools presented here might serve other researchers interested in monitoring, controlling and improving the reliability of their findings by implementation of good experimental quality practices in the untargeted metabolomics study.

Analytical challenges of untargeted GC-MS-based metabolomics and the critical issues in selecting the data processing strategy

Background: A challenge of metabolomics is data processing the enormous amount of information generated by sophisticated analytical techniques. The raw data of an untargeted metabolomic experiment are composited with unwanted biological and technical variations that confound the biological variations of interest. The art of data normalisation to offset these variations and/or eliminate experimental or biological biases has made significant progress recently. However, published comparative studies are often biased or have omissions. Methods: We investigated the issues with our own data set, using five different representative methods of internal standard-based, model-based, and pooled quality control-based approaches, and examined the performance of these methods against each other in an epidemiological study of gestational diabetes using plasma. Results: Our results demonstrated that the quality control-based approaches gave the highest data precision in all methods tested, and would be the method of choice for controlled experimental conditions. But for our epidemiological study, the model-based approaches were able to classify the clinical groups more effectively than the quality control-based approaches because of their ability to minimise not only technical variations, but also biological biases from the raw data. Conclusions: We suggest that metabolomic researchers should optimise and justify the method they have chosen for their experimental condition in order to obtain an optimal biological outcome.