Development of Multimarker Diagnostic Models from Metabolomics Analysis for Gestational Diabetes Mellitus (GDM)

Correlation between newborn weight and serum BCAAs in pregnant women with diabetes

BACKGROUND

Branched-chain amino acids (BCAAs), including leucine, isoleucine, and valine, are essential amino acids for mammals. Maternal BCAAs during pregnancy have been associated with newborn development. Meanwhile, BCAAs have been tightly linked with insulin resistance and diabetes in recent years. Diabetes in pregnancy is a common metabolic disorder. The current study aims to assess the circulating BCAA levels in pregnant women with diabetes and their relationship with neonatal development.

METHODS

The serum concentrations of BCAAs and their corresponding branched-chain α-keto acids (BCKAs) catabolites in 33 pregnant women with normal glucose tolerance, 16 pregnant women with type 2 diabetes before pregnancy (PDGM), and 15 pregnant women with gestational diabetes mellitus (GDM) were determined using a liquid chromatography system coupled to a mass spectrometer. The data were tested for normal distribution and homogeneity of variance before statistical analysis. Correlations were computed with the Pearson correlation coefficient.

RESULTS

The maternal serum BCAAs and BCKAs levels during late pregnancy were higher in women with PGDM than those in healthy women. Meanwhile, the circulating BCAAs and BCKAs showed no significant changes in women with GDM compared with those in healthy pregnant women. Furthermore, the circulating BCAA and BCKA levels in women with PGDM were positively correlated with the weight of the newborn. The circulating leucine level in women with GDM was positively correlated with the weight of the newborn. BCAA and BCKA levels in healthy pregnant women showed no correlation with newborn weight.

CONCLUSIONS

The serum BCAAs in pregnant women with diabetes, which was elevated in PGDM but not GDM, were positively correlated with newborn weight. These findings highlight potential approaches for early identification of high-risk individuals and interventions to reduce the risk of adverse pregnancy outcomes.

Retinol-binding protein 4 (RBP4) circulating levels and gestational diabetes mellitus: a systematic review and meta-analysis

Background

Gestational diabetes mellitus (GDM) is a prevalent condition where diabetes is diagnosed during pregnancy, affecting both maternal and fetal outcomes. Retinol-binding protein 4 (RBP4) is a circulating adipokine which belongs to the lipocalin family and acts as a specific carrier protein that delivers retinol (vitamin A) from the liver to the peripheral tissues. Growing data indicate that circulating RBP4 levels may positively correlate with GDM. Thus, this systematic review and meta-analysis aimed to investigate the potential relationship between circulating RBP4 levels and GDM when measured at various stages of pregnancy.

Methods

MEDLINE, CINAHL, EMCARE, EMBASE, Scopus, and Web of Science databases were searched to identify studies comparing pregnant women with and without GDM, whose circulating RBP4 levels were measured in at least one pregnancy trimester. Findings were reported using standardized mean difference (SMD) and random-effects models were used to account for variability among studies. Furthermore, the risk of bias was assessed using the RoBANS tool.

Results

Out of the 34 studies identified, 32 were included in the meta-analysis (seven with circulating RBP4 levels measured in the first trimester, 19 at 24-28 weeks, and 14 at >28 weeks of pregnancy). RBP4 levels were statistically higher in the GDM group than in controls when measured during all these pregnancy stages, with the noted RBP4 SMD being 0.322 in the first trimester (95% CI: 0.126-0.517; p < 0.001; 946 GDM cases vs. 1701 non-GDM controls); 0.628 at 24-28 weeks of gestation (95% CI: 0.290-0.966; p < 0.001; 1776 GDM cases vs. 1942 controls); and 0.875 at >28 weeks of gestation (95% CI: 0.252-1.498; p = 0.006; 870 GDM cases vs. 1942 non-GDM controls). Significant study heterogeneity was noted for all three pregnancy timepoints.

Conclusion

The present findings indicate consistently higher circulating RBP4 levels in GDM cases compared to non-GDM controls, suggesting the potential relevance of RBP4 as a biomarker for GDM. However, the documented substantial study heterogeneity, alongside imprecision in effect estimates, underscores the need for further research and standardization of measurement methods to elucidate whether RBP4 can be utilized in clinical practice as a potential GDM biomarker.

Systematic review registration

PROSPERO (CRD42022340097: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022340097).

Using metabolomics and proteomics to identify the potential urine biomarkers for prediction and diagnosis of gestational diabetes

Gestational diabetes mellitus (GDM) is one of the most common metabolic complications during pregnancy, threatening both maternal and fetal health. Prediction and diagnosis of GDM is not unified. Finding effective biomarkers for GDM is particularly important for achieving early prediction, accurate diagnosis and timely intervention. Urine, due to its accessibility in large quantities, noninvasive collection and easy preparation, has become a good sample for biomarker identification. In recent years, a number of studies using metabolomics and proteomics approaches have identified differential expressed urine metabolites and proteins in GDM patients. In this review, we summarized these potential urine biomarkers for GDM prediction and diagnosis and elucidated their role in development of GDM.

Advances in free fatty acid profiles in gestational diabetes mellitus

The morbidity of gestational diabetes mellitus (GDM) is increasing and is associated with adverse perinatal outcomes and long-term maternal and infant health. The exact mechanism underlying changes in plasma free fatty acid (FFA) profiles in patients with GDM is unknown. However, it is believed that changes in diet and lipid metabolism may play a role. Fatty acids contain many specific FFAs, and the type of FFA has different impacts on physiological processes; hence, determining changes in FFAs in individual plasma is essential. Alterations in FFA concentration or profile may facilitate insulin resistance. Additionally, some FFAs show potential to predict GDM in early pregnancy and are strongly associated with the growth and development of the fetus and occurrence of macrosomia. Here, we aimed to review changes in FFAs in women with GDM and discuss the relationship of FFAs with GDM incidence and adverse outcomes.

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.

Mass spectrometry-based "omics" technologies for the study of gestational diabetes and the discovery of new biomarkers

Gestational diabetes (GDM) is one of the most common complications occurring during pregnancy. Diagnosis is performed by oral glucose tolerance test, but harmonized testing methods and thresholds are still lacking worldwide. Short-term and long-term effects include obesity, type 2 diabetes, and increased risk of cardiovascular disease. The identification and validation of sensitidve, selective, and robust biomarkers for early diagnosis during the first trimester of pregnancy are required, as well as for the prediction of possible adverse outcomes after birth. Mass spectrometry (MS)-based omics technologies are nowadays the method of choice to characterize various pathologies at a molecular level. Proteomics and metabolomics of GDM were widely investigated in the last 10 years, and various proteins and metabolites were proposed as possible biomarkers. Metallomics of GDM was also reported, but studies are limited in number. The present review focuses on the description of the different analytical methods and MS-based instrumental platforms applied to GDM-related omics studies. Preparation procedures for various biological specimens are described and results are briefly summarized. Generally, only preliminary findings are reported by current studies and further efforts are required to determine definitive GDM biomarkers.

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.

Human Milk Oligosaccharides Variation in Gestational Diabetes Mellitus Mothers

Gestational diabetes mellitus (GDM) is a common disease of pregnancy, but with very limited knowledge of its impact on human milk oligosaccharides (HMOs) in breast milk. This study aimed to explore the lactational changes in the concentration of HMOs in exclusively breastfeeding GDM mothers and the differences between GDM and healthy mothers. A total of 22 mothers (11 GDM mothers vs. 11 healthy mothers) and their offspring were enrolled in the study and the levels of 14 HMOs were measured in colostrum, transitional milk, and mature milk. Most of the HMOs showed a significant temporal trend with decreasing levels over lactation; however, there were some exceptions for 2′-Fucosyllactose (2′-FL), 3-Fucosyllactose (3-FL), Lacto-N-fucopentaose II (LNFP-II), and Lacto-N-fucopentaose III (LNFP-III). Lacto-N-neotetraose (LNnT) was significantly higher in GDM mothers in all time points and its concentrations in colostrum and transitional milk were correlated positively with the infant’s weight-for-age Z-score at six months postnatal in the GDM group. Significant group differences were also found in LNFP-II, 3′-Sialyllactose (3′-SL), and Disialyllacto-N-tetraose (DSLNT) but not in all lactational periods. The role of differently expressed HMOs in GDM needs to be further explored by follow-up studies.

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.

Lipidomics in gestational diabetes mellitus

PURPOSE OF REVIEW

Epidemiological and mechanistic studies have reported relationships between blood lipids, mostly measured by traditional method in clinical settings, and gestational diabetes mellitus (GDM). Recent advances of high-throughput lipidomics techniques have made available more comprehensive lipid profiling in biological samples. This review aims to summarize evidence from prospective studies in assessing relations between blood lipids and GDM, and discuss potential underlying mechanisms.

RECENT FINDINGS

Mass spectrometry and nuclear magnetic resonance spectroscopy-based analytical platforms are extensively used in lipidomics research. Epidemiological studies have identified multiple novel lipidomic biomarkers that are associated with risk of GDM, such as certain types of fatty acids, glycerolipids, glycerophospholipids, sphingolipids, cholesterol, and lipoproteins. However, the findings are inconclusive mainly due to the heterogeneities in study populations, sample sizes, and analytical platforms. Mechanistic evidence indicates that abnormal lipid metabolism may be involved in the pathogenesis of GDM by impairing pancreatic β-cells and inducing insulin resistance through several etiologic pathways, such as inflammation and oxidative stress.

SUMMARY

Lipidomics is a powerful tool to study pathogenesis and biomarkers for GDM. Lipidomic biomarkers and pathways could help to identify women at high risk for GDM and could be potential targets for early prevention and intervention of GDM.

Metabolomics in gestational diabetes mellitus: A review

Gestational diabetes mellitus (GDM), a common complication of pregnancy, is a type of diabetes that is first detected and diagnosed during pregnancy. The incidence of GDM is increasing annually and is associated with many adverse pregnancy outcomes. Early prediction of the risk of GDM and intervention are thus important to reduce adverse pregnancy outcomes. Studies have revealed a correlation between the levels of amino acids, fatty acids, triglycerides, and other metabolites in early pregnancy and the occurrence of GDM. The development of high-throughput technologies used in metabolomics has enabled the detection of changes in the levels of small-molecule metabolites during early pregnancy, which can help reflect the overall physiological and pathological status of the body and explore the underlying mechanisms of the development of GDM. This review sought to summarize current research in this field and provide data for the development of strategies to manage GDM.

Dietary Impacts on Gestational Diabetes: Connection between Gut Microbiome and Epigenetic Mechanisms

Gestational diabetes mellitus (GDM) is one of the most common obstetric complications due to an increased level of glucose intolerance during pregnancy. The prevalence of GDM increases due to the obesity epidemic. GDM is also associated with an increased risk of gestational hypertension and preeclampsia resulting in elevated maternal and perinatal morbidity and mortality. Diet is one of the most important environmental factors associated with etiology of GDM. Studies have shown that the consumption of certain bioactive diets and nutrients before and during pregnancy might have preventive effects against GDM leading to a healthy pregnancy outcome as well as beneficial metabolic outcomes later in the offspring's life. Gut microbiome as a biological ecosystem bridges the gap between human health and diseases through diets. Maternal diets affect maternal and fetal gut microbiome and metabolomics profiles, which consequently regulate the host epigenome, thus contributing to later-life metabolic health in both mother and offspring. This review discusses the current knowledge regarding how epigenetic mechanisms mediate the interaction between maternal bioactive diets, the gut microbiome and the metabolome leading to improved metabolic health in both mother and offspring.

The association of maternal fat-soluble antioxidants in early pregnancy with gestational diabetes mellitus: a prospective cohort study

INTRODUCTION

Oxidative stress is linked to the development of gestational diabetes mellitus (GDM). Maternal antioxidant vitamins in early pregnancy may play a role in GDM occurrence. We aimed to investigate the associations of vitamins A and E in early pregnancy with the risk of GDM and to explore whether these antioxidant vitamins can be biomarkers for the early prediction of GDM.

METHODS

We carried out a prospective cohort study conducted in Beijing and enrolled pregnant women (n = 667) with vitamins A and E measurements at 9 weeks (IQR 8-10) of gestation and having one-step GDM screened with a 75-g oral glucose tolerance test between 24 and 28 weeks of gestation.

RESULTS

The vitamin A levels in early pregnancy were significantly higher in women with GDM than in those without GDM (p < 0.0001) and positively correlated with fasting blood glucose. In multivariate models, vitamin A levels were significantly associated with GDM (OR, 1.46; 95% CI: 1.14-1.88; p = 0.0032) per SD. A significant trend of risk effect on GDM risk across quartiles of vitamin A was observed (p_trend = 0.016). No significant association of serum vitamin E with GDM was observed overall. However, a noted trend of protective effect on GDM risk across quartiles of vitamin E/cholesterol ratio was observed (p_trend = 0.043). In ROC analysis, the multivariate model consisting of vitamin A and other risk factors showed the best predictive performance (AUC: 0.760; 95% CI: 0.705-0.815; p < 0.001).

CONCLUSIONS

Higher levels of vitamin A in early pregnancy were significantly associated with an increased risk of GDM. Vitamin A has the potential to be a biomarker indicating pathogenesis of GDM.

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.

The mediating role of maternal metabolites between lipids and adverse pregnancy outcomes of gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is one of the most common complications of pregnancy, and the demographics of pregnant women have changed in recent decades. GDM is a metabolic disease with short- and long-term adverse effects on both pregnant women and newborns. The metabolic changes and corresponding risk factors should be of great significance in understanding the pathological mechanism of GDM and reducing the incidence of adverse pregnancy outcomes in patients with GDM. The well-known GDM-associated lipids used in clinical tests, such as triglyceride (TG), are thought to play a major role in metabolic changes during GDM, which have a potential causal relationship with abnormal pregnancy outcomes of GDM. Therefore, this study analyzed the relationship between clinical lipid indicators, metabolic profiles, and abnormal pregnancy outcomes in GDM through mediation analysis. By constructing a metabolic atlas of 399 samples from GDM patients in different trimesters, we efficiently detected the key metabolites of adverse pregnancy outcomes and their mediating roles in bridging abnormal lipids and adverse pregnancy outcomes in patients with GDM. Our study confirmed that TG and total cholesterol were independent risk factors for adverse pregnancy outcomes in patients with GDM. Several key metabolites as mediators (e.g., gamma-linolenic acid, heptadecanoic acid, oleic acid, palmitic acid, and palmitoleic acid) have been identified as potential biomarkers for adverse pregnancy outcomes in patients with GDM. These metabolites mainly participate in the biosynthesis of unsaturated fatty acids, which may shed new light on the pathology of GDM and provide insights for further exploration of the molecular mechanisms underlying adverse pregnancy outcomes.

Maternal and Fetal Metabolites in Gestational Diabetes Mellitus: A Narrative Review

Gestational diabetes mellitus (GDM) is a major public health issue of our century due to its increasing prevalence, affecting 5% to 20% of all pregnancies. The pathogenesis of GDM has not been completely elucidated to date. Increasing evidence suggests the association of environmental factors with genetic and epigenetic factors in the development of GDM. So far, several metabolomics studies have investigated metabolic disruptions associated with GDM. The aim of this review is to highlight the usefulness of maternal metabolites as diagnosis markers of GDM as well as the importance of both maternal and fetal metabolites as prognosis biomarkers for GDM and GDM’s transition to type 2 diabetes mellitus T2DM.

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.

The Spatial Landscape of the Bacterial Community and Bile Acids in the Digestive Tract of Patients With Bile Reflux

Background

Bile reflux can lead to inflammation and increased intestinal metaplasia. Since bile acids can influence the gastrointestinal environment, it is possible that bile reflux may alter the gastric microbiota and potentially the oral or gut microbiota. Bile acids have a very complex interrelationship with microbiota. We aimed to explore the characteristics of the digestive tract microbiota and bile acids profile in bile reflux patients.

Methods

This study included 20 chronic gastritis patients with bile reflux and 20 chronic gastritis patients without bile reflux. Saliva, gastric fluid, and fecal samples were collected for bile acid testing. Buccal mucosal swabs, gastric mucosal tissues, and feces were collected for bacteria detection. The UPLC-MS/MS examined bile acids profiles. 16S rRNA gene sequencing was used to analyze the bacterial profile.

Results

Bilirubin in the blood increased in bile reflux patients. No other clinical factors were identified to be significantly associated with bile reflux. 12-DHCA, 6,7-diketo LCA, and βHDCA decreased while TUDCA increased in saliva of bile reflux patients. Streptococcus, Capnocytophaga, Neisseria, and Actinobacillus decreased in oral mucosa of bile reflux patients while Helicobacter, Prevotella, and Veillonella increased. Gastric bile acid levels were generally higher in bile reflux patients. Gastric mucosal microbiota was highly stable. The changes in fecal bile acids were insignificant. Bifidobacterium, Prevotella_2, Ruminococcus, Weissella, Neisseria, and Akkermansia decreased in fecal samples from bile reflux patients; while Alloprevotella, Prevotella_9, Parabacteroides, and Megamonas increased.

Conclusion

Our results demonstrate that bile reflux significantly alters the oral, gastric, and intestinal bile acids profiles but only influences the oral and gut microbiota composition. These findings indicate that bile reflux can modulate the gastrointestinal microbiota in a site-specific manner.

The CDKAL1 rs7747752-Bile Acids Interaction Increased Risk of Gestational Diabetes Mellitus: A Nested Case-Control Study

AIMS

The study aimed to explore additive interactions of CDKAL1 rs7747752 and GUDCA/DCA for GDM risk and whether the interactive effects on the risk of GDM was mediated via increasing lysophosphatidylcholines (LPC) 18:0 and/or saturated fatty acid (SFA) 16:0.

METHODS

A 1:1 age-matched study nested in a prospective cohort of pregnant women (207 pairs) was organized in Tianjin, China. Additive interactions were used to test interaction effects while mediation analyses and Sobel tests were used to test mediation effects of LPC18:0 and SFA16:0 between copresence of rs7747752 and low GUDCA/DCA, and GDM risk.

RESULTS

The CDKAL1 rs7747752 was associated with GDM (P<0.05). The rs7747752 C polymorphism markedly enhanced ORs of low GUDCA from 4.04 (0.72-22.8) to 9.02 (1.63-49.7) and low DCA from 1.67 (0.68-4.11) to 4.24 (1.84-9.76), both with significant additive interactions. Further adjustment for LPC18:0 attenuated the interactive effects of rs7747752 and low DCA, with a significant mediation effect (P=0.003). High SFA16:0 did not mediate the interactive effects of rs7747752 and low DCA/GUDCA on GDM risk.

CONCLUSIONS

The CDKAL1 rs7747752 C carrier status and low GUDCA/DCA had significant additive interactions on the risk of GDM with the effect from interaction with DCA being partially mediated via increasing LPC18:0.

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.

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.

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.

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.

Current progress in metabolomics of gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is one of the most common metabolic disorders of pregnancy and can cause short- and long-term adverse effects in both pregnant women and their offspring. However, the etiology and pathogenesis of GDM are still unclear. As a metabolic disease, GDM is well suited to metabolomics study, which can monitor the changes in small molecular metabolites induced by maternal stimuli or perturbations in real time. The application of metabolomics in GDM can be used to discover diagnostic biomarkers, evaluate the prognosis of the disease, guide the application of diet or drugs, evaluate the curative effect, and explore the mechanism. This review provides comprehensive documentation of metabolomics research methods and techniques as well as the current progress in GDM research. We anticipate that the review will contribute to identifying gaps in the current knowledge or metabolomics technology, provide evidence-based information, and inform future research directions in GDM.

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.

Plasma Metabolomics of Acute Coronary Syndrome Patients Based on Untargeted Liquid Chromatography-Mass Spectrometry

Background: Acute coronary syndrome (ACS) is the main cause of death and morbidity worldwide. The present study aims to investigate the altered metabolites in plasma from patients with ACS and sought to identify metabolic biomarkers for ACS. Methods: The plasma metabolomics profiles of 284 ACS patients and 130 controls were carried out based on an untargeted liquid chromatography coupled with tandem mass spectrometry (LC-MS) approach. Multivariate statistical methods, pathway enrichment analysis, and univariate receiver operating characteristic (ROC) curve analysis were performed. Results: A total of 328 and 194 features were determined in positive and negative electrospray ionization mode in the LC-MS analysis, respectively. Twenty-eight metabolites were found to be differentially expressed, in ACS patients relative to controls (p < 0.05). Pathway analysis revealed that these metabolites are mainly involved in synthesis and degradation of ketone bodies, phenylalanine metabolism, and arginine and proline metabolism. Furthermore, a diagnostic model was constructed based on the metabolites identified and the areas under the curve (AUC) for 5-oxo-D-proline, creatinine, phosphatidylethanolamine lyso 16:0, and LPC (20:4) range from 0.764 to 0.844. The higher AUC value of 0.905 was obtained for the combined detection of phosphatidylethanolamine lyso 16:0 and LPC (20:4). Conclusions: Differential metabolic profiles may be useful for the effective diagnosis of ACS and may provide additional insight into the molecular mechanisms underlying ACS.

Reduced glycodeoxycholic acid levels are associated with negative clinical outcomes of gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is characterized by glycemia and insulin disorders. Bile acids (BAs) have emerged as vital signaling molecules in glucose metabolic regulation. BA change in GDM is still unclear, which exerts great significance to illustrate the change of BAs in GDM. GDM patients and normal pregnant women were enrolled during the oral glucose tolerance test (OGTT) screening period. Fasting serums were sampled for the measurement of BAs. BA metabolism profiles were analyzed in both pregnant women with GDM and those with normal glucose tolerance (NGT). Delivery characteristics, delivery gestational age, and infant birthweight were extracted from medical records. GDM patients presented distinctive features compared with NGT patients, including higher body mass index (BMI), elevated serum glucose concentration, raised insulin (both fasting and OGTT), and increased hemoglobin A1c (HbA1c) levels. Higher homeostasis model assessment of insulin resistance (HOMA-IR) and decreased β-cell compensation (i.e., oral disposition index (DIo)) were also prevalent in this group. Total BAs (TBAs) remained stable, but glycodeoxycholic acid (GDCA) and taurodeoxycholic acid (TDCA) levels declined significantly in GDM. GDCA was inversely correlated with HOMA-IR and positively correlated with DIo. No obvious differences in clinical outcome between the GDM and NGT groups were observed. However, GDM patients with high HOMA-IR and low DIo tended to have a higher cesarean delivery rate and younger delivery gestational age. In conclusion, GDCA provides a valuable biomarker to evaluate HOMA-IR and DIo, and decreased GDCA levels predict poorer clinical outcomes for GDM.

Altered gut bacterial and metabolic signatures and their interaction in gestational diabetes mellitus

Emerging evidence indicates that the gut microbiome can modulate metabolic homeostasis, and thus may influence the development of gestational diabetes mellitus (GDM). However, whether and how the gut microbiome and its correlated metabolites change in GDM is uncertain. Herein we compare the gut microbial compositions, and fecal and urine metabolomes, of 59 patients with GDM versus 48 pregnant healthy controls (HCs). We showed that the microbial and metabolic signatures of GDM patients were significantly different from those of HCs. Compared to HCs, the GDM subjects were characterized by enriched bacterial operational taxonomic units (OTUs) of the family Lachnospiraceae, and depleted OTUs of the families Enterobacteriaceae and Ruminococcaceae. Some altered gut microbes were significantly correlated with glucose values and fetal ultrasonography indexes. Moreover, we identified four fecal and 15 urine metabolites that discriminate GDM from HC. These differential metabolites are mainly involved in carbohydrate and amino acid metabolism. Significantly, co-occurrence network analysis revealed that Lachnospiraceae and Enterobacteriaceae bacterial OTUs formed strong co-occurring relationships with metabolites involved in carbohydrate and amino acid metabolism, suggesting that disturbed gut microbiome may mediate GDM. Furthermore, we identified a novel combinatorial marker panel that could distinguish GDM from HC subjects with high accuracy. Together our findings demonstrate that altered microbial composition and metabolic function may be relevant to the pathogenesis and pathophysiology of GDM.

Associations of Arginine with Gestational Diabetes Mellitus in a Follow-Up Study

In the reported study we applied the targeted metabolomic profiling employing high pressure liquid chromatography coupled with triple quadrupole tandem mass spectrometry (HPLC–MS/MS) to understand the pathophysiology of gestational diabetes mellitus (GDM), early identification of women who are at risk of developing GDM, and the differences in recovery postpartum between these women and normoglycemic women. We profiled the peripheral blood from patients during the second trimester of pregnancy and three months, and one year postpartum. In the GDM group Arg, Gln, His, Met, Phe and Ser were downregulated with statistical significance in comparison to normoglycemic (NGT) women. From the analysis of the association of all amino acid profiles of GDM and NGT women, several statistical models predicting diabetic status were formulated and compared with the literature, with the arginine-based model as the most promising of the screened ones (area under the curve (AUC) = 0.749). Our research results have shed light on the critical role of arginine in the development of GDM and may help in precisely distinguishing between GDM and NGT and earlier detection of GDM but also in predicting women with the increased type 2 diabetes mellitus (T2DM) risk.

Amino acids levels in early pregnancy predict subsequent gestational diabetes

BACKGROUND

We aimed to estimate the performance of amino acids levels in predicting the risk of subsequent gestational diabetes mellitus (GDM).

METHODS

A total of 431 women at 12 to 16 weeks of gestation in the Department of Obstetrics and Gynecology of the Second Affiliated Hospital of Soochow University were recruited. High-performance liquid chromatography electrospray tandem mass spectrometry was used to measure amino acids levels in maternal blood at 12 to 16 weeks of gestation. At 24 to 28 weeks of gestation, all participants were administered 75-g oral glucose tolerance tests for the diagnosis of GDM.

RESULTS

Alanine, isoleucine, and tyrosine levels in early pregnancy were significantly different between women who developed GDM and those who remained normal glucose tolerant. Logistic regressions showed that after adjustments for age, parity, body mass index, family history of diabetes, γ-glutamyltranspeptidase, triglycerides, fasting glucose and fasting insulin levels, alanine (odds ratio [OR], 1.46; 95% CI, 1.05-2.04; P = .027), isoleucine (OR, 1.48; 95% CI, 1.12-1.96; P = .0062), and tyrosine (OR, 1.46; 95% CI, 1.07-2.03; P = .020) levels in early pregnancy were independently associated with subsequent GDM. The addition of isoleucine and tyrosine into the conventional model improved the area under curve from 0.692 to 0.737 (P = .036) and significantly increased the net reclassification improvement (+13.7%, P = .0025).

CONCLUSIONS

The present study suggests that elevated isoleucine, tyrosine, and alanine levels are independently and significantly associated with subsequent incidence of GDM. New models including conventional risk factors, isoleucine and tyrosine levels in early pregnancy might help physicians identify high-risk population of GDM.

Adipokines in early and mid-pregnancy and subsequent risk of gestational diabetes: a longitudinal study in a multiracial cohort

INTRODUCTION

Several adipokines are implicated in the pathophysiology of gestational diabetes mellitus (GDM), however, longitudinal data in early pregnancy on many adipokines are lacking. We prospectively investigated the association of a panel of adipokines in early and mid-pregnancy with GDM risk.

RESEARCH DESIGN AND METHODS

Within the National Institute of Child Health and Human Development (NICHD) Fetal Growth Studies-Singletons cohort (n=2802), a panel of 10 adipokines (plasma fatty acid binding protein-4 (FABP4), chemerin, interleukin-6 (IL-6), leptin, soluble leptin receptor (sOB-R), adiponectin, omentin-1, vaspin, and retinol binding protein-4) were measured at gestational weeks (GWs) 10-14, 15-26, 23-31, and 33-39 among 107 GDM cases (ascertained on average at GW 27) and 214 non-GDM controls. Conditional logistic regression was used to estimate ORs of each adipokine and GDM, controlling for known GDM risk factors including pre-pregnancy body mass index.

RESULTS

Throughout pregnancy changes in chemerin, sOB-R, adiponectin, and high-molecular-weight adiponectin (HMW-adiponectin) concentrations from 10-14 to 15-26 GWs were significantly different among GDM cases compared with non-GDM controls. In early and mid-pregnancy, FABP4, chemerin, IL-6 and leptin were positively associated with increased GDM risk. For instance, at 10-14 GWs, the OR comparing the highest versus lowest quartile (ORQ4-Q1) of FABP4 was 3.79 (95% CI 1.63 to 8.85). In contrast, in both early and mid-pregnancy adiponectin (eg, ORQ4-Q1 0.14 (0.05, 0.34) during 10-14 GWs) and sOB-R (ORQ4-Q1 0.23 (0.11, 0.50) during 10-14 GWs) were inversely related to GDM risk. At 10-14 GWs a model that included conventional GDM risk factors and FABP4, chemerin, sOB-R, and HMW-adiponectin improved the estimated prediction (area under the curve) from 0.71 (95% CI 0.66 to 0.77) to 0.77 (95% CI 0.72 to 0.82).

CONCLUSIONS

A panel of understudied adipokines including FABP4, chemerin, and sOB-R may be implicated in the pathogenesis of GDM with significant associations detected approximately 10-18 weeks before typical GDM screening.

Plasma N-acetylaspartate: Development and validation of a quantitative assay based on HPLC-MS-MS and sample derivatization

N-acetylaspartate is a human endogenous compound synthesized by neurons, which is involved in neuronal metabolism. It is used as a marker in brain magnetic resonance spectroscopy to investigate several neurological and metabolic disorders, that can be related to a variation of its concentration with respect to reference values. N-acetylaspartate is present also in biological fluids, such as plasma, urine, and cerebrospinal fluid, where it can be quantified. Here we describe the development and validation, in compliance with the EMA guidelines, of a novel assay method for the quantification of N-acetylaspartate in plasma based on tandem mass spectrometry coupled to liquid chromatography. Its peculiarity lies in the fact that sample preparation includes an esterification step, which significantly improves the chromatographic performances and, consequently, also the method sensitivity, reproducibility and accuracy. Instrumental LLOQ is 0.06 ng/mL, i.e. at least 300 times lower than the medium N-acetylaspartate concentration in samples, accuracy is in the range 98-103%, while precision lies between 1 and 3%. The method robustness was tested in about 1000 injections of plasma samples, 96 of which were used also to assess the reference ranges in control subjects (16.46-63.40 ng/mL). Controls were then compared to plasma samples from type 2 diabetic patients. Contrary to brain magnetic resonance spectroscopy, which demonstrated a decrease in the N-acetylaspartate levels in right frontal and parieto-temporal region of type 2 diabetic patients, plasma analysis showed no statistical difference with respect to controls. However, the method here described can be profitably used in studies concerning different disorders with CNS involvement, as confirmed by reports available in the literature.

Elevated fasting vs post-load glucose levels and pregnancy outcomes in gestational diabetes: a population-based study

AIMS

To examine the relative association between fasting plasma glucose vs post-load (1-h and 2-h) glucose levels based on the oral glucose tolerance test in pregnancy and large-for-gestational-age and hypertensive disorders of pregnancy outcomes.

METHODS

All live singleton births between October 2008 and December 2014 in Alberta, Canada were included. Gestational diabetes mellitus was diagnosed using Diabetes Canada criteria. Logistic regression models were used to examine the association between fasting plasma glucose vs post-load values and large-for-gestational-age infants and hypertensive disorders of pregnancy after adjusting for maternal characteristics and pharmaceutical intervention in gestational diabetes pregnancies.

RESULTS

Among 257 547 pregnancies, 208 344 (80.9%) had negative 50-g glucose challenge tests, 36 261 (14.1%) had negative 75-g oral glucose tolerance tests, and 12 942 (5.0%) had gestational diabetes based on either elevated fasting plasma glucose (n=4130, 1.6%) or elevated 1-h and/or 2-h oral glucose tolerance test values (n=8812, 3.4%). Large-for-gestational-age and hypertensive disorders of pregnancy rates were 8.1% and 5.1% in negative glucose challenge test pregnancies, 11.0% and 7.0% in negative oral glucose tolerance test pregnancies, 22.4% and 11.9% in gestational diabetes pregnancies with elevated fasting plasma glucose, and 9.1% and 8% in gestational diabetes pregnancies with elevated post-load levels, respectively. Among gestational diabetes pregnancies, those with elevated fasting plasma glucose were at higher risk of large-for-gestational age (adjusted odds ratio 2.66, 95% CI 2.39-2.96) and hypertensive disorders of pregnancy (adjusted odds ratio 1.51, 95% CI 1.33-1.72) outcomes relative to pregnancies with post-load glucose elevations only. Fasting plasma glucose remained significantly associated with adverse outcomes in gestational diabetes pregnancies with and without pharmacological intervention.

CONCLUSIONS

Elevated fasting plasma glucose in women with gestational diabetes is a stronger predictor of large-for-gestational-age and hypertensive disorders of pregnancy outcomes than elevated post-load glucose.

An overview of the current progress, challenges, and prospects of human biomonitoring and exposome studies

Human Biomonitoring (HB), the process for determining whether and to what extent chemical substances penetrated our bodies, serves as a useful tool to quantify human exposure to pollutants. In cases of nutrition and physiologic status, HB plays a critical role in the identification of excess or deficiency of essential nutrients. In pollutant HB studies, levels of substances measured in body fluids (blood, urine, and breast milk) or tissues (hair, nails or teeth) aid in the identification of potential health risks or associated adverse effects. However, even as a widespread practice in several countries, most HB studies reflect exposure to a single compound or mixtures which are measured at a single time point in lifecycle. On the other hand, throughout an individual's lifespan, the contact with different physical, chemical, and social stressors occurs at varying intensities, differing times and durations. Further, the interaction between stressors and body receptors leads to dynamic responses of the entire biological system including proteome, metabolome, transcriptome, and adductome. Bearing this in mind, a relatively new vision in exposure science, defined as the exposome, is postulated to expand the traditional practice of measuring a single exposure to one or few chemicals at one-time point to an approach that addresses measures of exposure to multiple stressors throughout the lifespan. With the exposome concept, the science of exposure advances to an Environment-Wide Association Perspective, which might exhibit a stronger relationship with good health or disease conditions for an individual (phenotype). Thus, this critical review focused on the current progress of HB and exposome investigations, anticipating some challenges, strategies, and future needs to be taken into account for designing future surveys.

Metabolomics Studies To Decipher Stress Responses in Mycobacterium smegmatis Point to a Putative Pathway of Methylated Amine Biosynthesis

Mycobacterium smegmatis, the saprophytic soil mycobacterium, is routinely used as a surrogate system to study the human pathogen Mycobacterium tuberculosis It has also been reported as an opportunistic pathogen in immunocompromised hosts. In addition, it can exist in several ecological setups, thereby suggesting its capacity to adapt to a variety of environmental cues. In this study, we employed untargeted proton nuclear magnetic resonance (¹H-NMR)-based metabolomics to identify metabolites and metabolic pathways critical for early adaptive responses to acidic stress, oxidative stress, and nutrient starvation in Mycobacterium smegmatis We identified 31, 20, and 46 metabolites that showed significant changes in levels in response to acidic, oxidative, and nutrient starvation stresses, respectively. Pathway analyses showed significant perturbations in purine-pyrimidine, amino-acid, nicotinate-nicotinamide, and energy metabolism pathways. Besides these, differential levels of intermediary metabolites involved in α-glucan biosynthesis pathway were observed. We also detected high levels of organic osmolytes, methylamine, and betaine during nutrient starvation and oxidative stress. Further, tracing the differential levels of these osmolytes through computational search tools, gene expression studies (using reverse transcription-PCR [RT-PCR]), and enzyme assays, we detected the presence of a putative pathway of biosynthesis of betaine, methylamine, and dimethylamine previously unreported in Mycobacterium smegmatis IMPORTANCE Alterations in metabolite levels provide fast and direct means to regulate enzymatic reactions and, therefore, metabolic pathways. This study documents, for the first time, the metabolic changes that occur in Mycobacterium smegmatis as a response to three stresses, namely, acidic stress, oxidative stress, and nutrient starvation. These stresses are also faced by intracellular mycobacteria during infection and therefore may be extended to frame therapeutic interventions for pathogenic mycobacteria. In addition to the purine-pyrimidine, amino acid, nicotinate-nicotinamide, and energy metabolism pathways that were found to be affected in response to different stresses, a novel putative methylamine biosynthesis pathway was identified to be present in Mycobacterium smegmatis.

Assessing the Effectiveness of Direct Data Merging Strategy in Long-Term and Large-Scale Pharmacometabonomics

Because of the extended period of clinic data collection and huge size of analyzed samples, the long-term and large-scale pharmacometabonomics profiling is frequently encountered in the discovery of drug/target and the guidance of personalized medicine. So far, integration of the results (ReIn) from multiple experiments in a large-scale metabolomic profiling has become a widely used strategy for enhancing the reliability and robustness of analytical results, and the strategy of direct data merging (DiMe) among experiments is also proposed to increase statistical power, reduce experimental bias, enhance reproducibility and improve overall biological understanding. However, compared with the ReIn, the DiMe has not yet been widely adopted in current metabolomics studies, due to the difficulty in removing unwanted variations and the inexistence of prior knowledges on the performance of the available merging methods. It is therefore urgently needed to clarify whether DiMe can enhance the performance of metabolic profiling or not. Herein, the performance of DiMe on 4 pairs of benchmark datasets was comprehensively assessed by multiple criteria (classification capacity, robustness and false discovery rate). As a result, integration/merging-based strategies (ReIn and DiMe) were found to perform better under all criteria than those strategies based on single experiment. Moreover, DiMe was discovered to outperform ReIn in classification capacity and robustness, while the ReIn showed superior capacity in controlling false discovery rate. In conclusion, these findings provided valuable guidance to the selection of suitable analytical strategy for current metabolomics.

Biomarker Discovery for Immunotherapy of Pituitary Adenomas: Enhanced Robustness and Prediction Ability by Modern Computational Tools

Pituitary adenoma (PA) is prevalent in the general population. Due to its severe complications and aggressive infiltration into the surrounding brain structure, the effective management of PA is required. Till now, no drug has been approved for treating non-functional PA, and the removal of cancerous cells from the pituitary is still under experimental investigation. Due to its superior specificity and safety profile, immunotherapy stands as one of the most promising strategies for dealing with PA refractory to the standard treatment, and various studies have been carried out to discover immune-related gene markers as target candidates. However, the lists of gene markers identified among different studies are reported to be highly inconsistent because of the greatly limited number of samples analyzed in each study. It is thus essential to substantially enlarge the sample size and comprehensively assess the robustness of the identified immune-related gene markers. Herein, a novel strategy of direct data integration (DDI) was proposed to combine available PA microarray datasets, which significantly enlarged the sample size. First, the robustness of the gene markers identified by DDI strategy was found to be substantially enhanced compared with that of previous studies. Then, the DDI of all reported PA-related microarray datasets were conducted to achieve a comprehensive identification of PA gene markers, and 66 immune-related genes were discovered as target candidates for PA immunotherapy. Finally, based on the analysis of human protein⁻protein interaction network, some promising target candidates (GAL, LMO4, STAT3, PD-L1, TGFB and TGFBR3) were proposed for PA immunotherapy. The strategy proposed together with the immune-related markers identified in this study provided a useful guidance for the development of novel immunotherapy for PA.

Plasma Levels of Free Fatty Acids in Women with Gestational Diabetes and Its Intrinsic and Extrinsic Determinants: Systematic Review and Meta-Analysis

BACKGROUND

Free fatty acids, also known as nonesterified fatty acids, are proinflammatory molecules that induce insulin resistance in nonpregnant individuals. Nevertheless, the concentration of these molecules has not been systematically addressed in pregnant women.

OBJECTIVE

This meta-analysis is aimed at evaluating the difference in free fatty acid plasma levels between women with gestational diabetes and healthy pregnant controls and their intrinsic and extrinsic determinants.

METHODS

We performed a systematic search to find relevant studies published in English and Spanish using PubMed, SCOPUS, and ISI Web of Knowledge. We included observational studies measuring the mean plasma levels of free fatty acids among gestational diabetes and healthy pregnant women, with at least ten subjects being analyzed in each group. The standardized mean difference (SMD) by random effects modeling was used. Heterogeneity was assessed using Cochran's Q, H, and I ² statistics.

RESULTS

Among the 290 identified studies, twelve were selected for analysis. A total of 2426 women were included, from which 21% were diagnosed as having gestational diabetes. There were significantly higher levels of free fatty acids among women with gestational diabetes (SMD: 0.86; 0.54-1.18; p < 0.001) when compared to healthy pregnant controls and between-study heterogeneity (I ² = 91%). The metaregression analysis showed that the gestational age at inclusion was the only cofactor influencing the mean levels of free fatty acids, indicating a trend towards lower plasma levels of free fatty acids later in gestation (estimate: -0.074; -0.143 to -0.004; p = 0.036). No significant publication bias was found nor a trend towards greater results in small studies.

CONCLUSIONS

Women with gestational diabetes have higher levels of free fatty acids when compared to healthy pregnant controls. More investigation is needed to assess the potential role of free fatty acids in the prediction of gestational diabetes earlier in pregnancy.

The Role of Inflammation in the Development of GDM and the Use of Markers of Inflammation in GDM Screening

Gestational diabetes mellitus is a hyperglycaemic state first recognised in pregnancy. GDM affects both mother and child. Women with GDM and their new-borns are at risk of developing type 2 diabetes in the future. The screening and diagnostic criteria for GDM are inconsistent and thus novel biomarkers of GDM are required to strengthen the screening and diagnostic processes in GDM. Chronic low-grade inflammation is linked to the majority of the well-established risk factors of GDM such as old age, obesity and PCOS. This review provides an overview of the present knowledge on the pathology of GDM, the screening criteria applied, the role of inflammation in the development of GDM and the use of markers of inflammation namely cytokines, oxidative stress markers, lipids, amino acids and iron markers in screening and diagnosis of GDM.

The fecal metabolome is associated with gestational diabetes mellitus

Dysbiosis of gut microbiota has been linked to gestational diabetes mellitus (GDM), and grows as a resource for GDM biomarkers. However, the contributions of gut microbiota to GDM remain incompletely understood. Metabolites are key messengers in the interactions between gut microbiota and the host. Metabolomics is emerging as an essential tool in exploring the contributions of gut microbiota to diseases. In this study, we performed ¹H-NMR based metabolomics on the feces of 62 pregnant women, including 31 women with GDM, and 31 women as the non-diabetes (NDM) control. Using Principle Component Analysis (PCA) and Orthogonal Projection to Latent Structures Discrimination Analysis (OPLS-DA), we observed clear cluster separation of the fecal metabolome between women with GDM and the NDM control. We further applied several feature selection methods to find five fecal metabolites contributing to the cluster separation of the fecal metabolome. These five metabolites, namely dibutyl decanedioate, N-acetylgalactosamine-4-sulphate, homocysteine, l-malic acid, and butanone, were significantly correlated with the clinical indices of GDM. Metabolite enrichment and pathway analysis on the five metabolites suggested that the fecal citrate cycle and sulfur metabolism were correlated with GDM. The results of this study demonstrated that disorders in the fecal metabolome are associated with GDM.

Fecal metabolome could separate women with GDM from the non-diabetic control.

Bile acid metabolites in early pregnancy and risk of gestational diabetes in Chinese women: A nested case-control study

Background

Bile acid metabolism plays an important role in metabolism but it is uncertain whether bile acid metabolites in early pregnancy are associated with risk of gestational diabetes mellitus (GDM).

Methods

We organized a 1:1 case-control study nested in a prospective cohort of 22,302 pregnant women recruited from 2010 to 2012 in China: 243 women with GDM were matched with 243 non-GDM controls on age (±1 year). Conditional logistic regression and restricted cubic spline were used to examine full-range associations of bile acid metabolites with GDM.

Findings

All the 9 detectable bile acids were inversely associated with the risk of GDM, among them, 8 in nonlinear and one in largely linear manners in multivariable analysis. Glycoursodeoxycholic acid (GUDCA) at ≤0.07 nmol/mL and deoxycholic acid (DCA) at ≤0.28 nmol/mL had threshold effects and their decreasing levels below the cutoff points were associated with rapid rises in the risk of GDM. In traditional risk factor model, the stepwise procedure identified that GUDCA ≤ 0.07 nmol/mL and DCA ≤ 0.280 nmol/mL were still significant (OR: 6.84, 95%CI: 1.10–42.48 & 2.06, 1.26–3.37), while other bile acids were not. Inclusion of the two bile acids in the model increased the area under operating characteristic's curve from 0.69 to 0.76 (95% CI: 0.71–0.80) (P < .05).

Interpretation

Serum GUDCA ≤ 0.07 nmol/mL and DCA ≤ 0.28 nmol/mL in early pregnancy were independently associated with increased risk of GDM in Chinese pregnant women.

Funding

Talent Recruitment Scheme grant of Tianjin Medical University and National Key Research and Development Program, etc.