Comprehensive analysis of serum metabolites in gestational diabetes mellitus by UPLC/Q-TOF-MS

Proteomic analysis of milk fat globule membrane proteins in mature human milk of women with and without gestational diabetes mellitus

Milk fat globule membrane proteins (MFGMP) in human milks have positive effects on infant's health. As gestational diabetes mellitus (GDM) causes variations in MFGMP, it is essential to understand the effects of GDMon MFGMP. This study aims to investigate and compare the MFGMP (>3 months postpartum) of GDM and non-GDM (NGDM) women using four-dimensional-data-independent-acquisition proteomics technology. Principal component analysis shows significant differences in the MFGMP of GDM and NGDM women. A total of 4747 MFGMP were identified in maturehuman milk of GDM and NGDM women. Among these proteins, 174 differentially expressed proteins (DEPs) were identified in MFGM of GDM and NGDM women. Albumin (FC = 7.96) and transthyretin (FC = 2.57) which are related to insulin resistance and involved in thyroid hormone synthesis, are significantly up-regulated in MFGMP of GDM mothers indicating insulin resistance, imbalance of glucose homeostasis and poor glucose metabolism might persist in postpartum period.

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.

Refining the diagnosis of gestational diabetes mellitus: a systematic review and meta-analysis

BACKGROUND

Perinatal outcomes vary for women with gestational diabetes mellitus (GDM). The precise factors beyond glycemic status that may refine GDM diagnosis remain unclear. We conducted a systematic review and meta-analysis of potential precision markers for GDM.

METHODS

Systematic literature searches were performed in PubMed and EMBASE from inception to March 2022 for studies comparing perinatal outcomes among women with GDM. We searched for precision markers in the following categories: maternal anthropometrics, clinical/sociocultural factors, non-glycemic biochemical markers, genetics/genomics or other -omics, and fetal biometry. We conducted post-hoc meta-analyses of a subset of studies with data on the association of maternal body mass index (BMI, kg/m2) with offspring macrosomia or large-for-gestational age (LGA).

RESULTS

A total of 5905 titles/abstracts were screened, 775 full-texts reviewed, and 137 studies synthesized. Maternal anthropometrics were the most frequent risk marker. Meta-analysis demonstrated that women with GDM and overweight/obesity vs. GDM with normal range BMI are at higher risk of offspring macrosomia (13 studies [n = 28,763]; odds ratio [OR] 2.65; 95% Confidence Interval [CI] 1.91, 3.68), and LGA (10 studies [n = 20,070]; OR 2.23; 95% CI 2.00, 2.49). Lipids and insulin resistance/secretion indices were the most studied non-glycemic biochemical markers, with increased triglycerides and insulin resistance generally associated with greater risk of offspring macrosomia or LGA. Studies evaluating other markers had inconsistent findings as to whether they could be used as precision markers.

CONCLUSIONS

Maternal overweight/obesity is associated with greater risk of offspring macrosomia or LGA in women with GDM. Pregnancy insulin resistance or hypertriglyceridemia may be useful in GDM risk stratification. Future studies examining non-glycemic biochemical, genetic, other -omic, or sociocultural precision markers among women with GDM are warranted.

Sardinian Infants of Diabetic Mothers: A Metabolomics Observational Study

Gestational diabetes mellitus (GDM) is a condition characterized by glucose intolerance, with hyperglycemia of varying severity with onset during pregnancy. An uncontrolled GDM can lead to an increased risk of morbidity in the fetus and newborn, and an increased risk of obesity or developing type 2 diabetes, hypertension or neurocognitive developmental impairment in adulthood. In this study, we used nuclear magnetic resonance (NMR) spectroscopy and gas chromatography-mass spectrometry (GS-MS) to analyze the urinary metabolomic profile of newborns of diabetic mothers (NDMs) with the aim of identifying biomarkers useful for the monitoring of NDMs and for early diagnosis of predisposition to develop related chronic diseases. A total of 26 newborns were recruited: 21 children of diabetic mothers, comprising 13 in diet therapy (NDM-diet) and 8 in insulin therapy (NDM-insulin), and 5 control children of non-diabetic mothers (CTR). Urine samples were collected at five time points: at birth (T1), on the third day of life (T2), one week (T3), one month (T4) and six months postpartum (T5). At T1, variations were observed in the levels of seven potential biomarkers (acetate, lactate, glycylproline/proline, isocitrate, N,N-dimethylglycine, N-acetylglucosamine and N-carbamoyl-aspartate) in NMD-insulin infants compared to NDM-diet and CTR infants. In particular, the altered metabolites were found to be involved in several metabolic pathways such as citrate metabolism, glycine, serine and threonine metabolism, arginine and proline metabolism, amino sugar and nucleotide sugar metabolism, and pyruvate metabolism. In contrast, these changes were not visible at subsequent sampling times. The impact of early nutrition (maternal and formula milk) on the metabolomic profile was considered as a potential contributing factor to this finding.

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.

Maternal plasma diacylglycerols and triacylglycerols in the prediction of gestational diabetes mellitus

OBJECTIVE

To define the lipidomic profile in plasma across pregnancy, and identify lipid biomarkers for gestational diabetes mellitus (GDM) prediction in early pregnancy.

DESIGN

Case-control study.

SETTING

Tertiary referral maternity unit.

POPULATION OR SAMPLE

Plasma samples from 100 GDM and 100 normal glucose tolerance (NGT) women, divided into a training set (GDM first trimester = 50, GDM second trimester = 40, NGT first trimester = 50, NGT second trimester = 50) and a validation set (GDM first trimester = 45, GDM second trimester = 34, NGT first trimester = 44, NGT second trimester = 40).

METHODS

Plasma samples were collected in the first (11⁺⁰ to 13⁺⁶ weeks), second (19⁺⁰ to 24⁺⁶ weeks), and third trimesters (30⁺⁰ to 34⁺⁶ weeks), and tested by ultra-high-performance liquid chromatography coupled with electrospray ionisation-quadrupole-time of flight-mass spectrometry; The GDM prediction model was established by the machine-learning method of random forest.

MAIN OUTCOME MEASURES

Gestational diabetes mellitus.

RESULTS

In both the GDM and NGT group, lyso-glycerophospholipids were down-regulated, whereas ceramides, sphingomyelins, cholesteryl ester, diacylglycerols (DGs) and triacylglycerols (TGs) and glucosylceramide were up-regulated across the three trimesters of pregnancy. In the training dataset, seven TGs and five DGs demonstrated good performance in the prediction of GDM in the first and second trimesters (area under the curve [AUC] = 0.96 with 95% confidence interval [CI] of 0.93-1 and AUC = 0.97 with 95% CI of 0.95-1, respectively), independent of maternal body mass index (BMI) and ethnicity. In the validation dataset, the predictive model achieved an AUC of 0.88 and 0.94 at the first and second trimesters, respectively.

CONCLUSIONS

Our results have proposed new lipid biomarkers for the first trimester prediction of GDM, independent of ethnicity and BMI.

Metabolomics can provide new insights into perinatal nutrition

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

Unique Metabolic Profiles Associate with Gestational Diabetes and Ethnicity in Low- and High-Risk Women Living in the UK

BACKGROUND

Gestational diabetes mellitus (GDM) is the most common global pregnancy complication; however, prevalence varies substantially between ethnicities, with South Asians (SAs) experiencing up to 3 times the risk of the disease compared with white Europeans (WEs). Factors driving this discrepancy are unclear, although the metabolome is of great interest as GDM is known to be characterized by metabolic dysregulation.

OBJECTIVES

The primary aim was to characterize and compare the metabolic profiles of GDM in SA and WE women (at <28 wk of gestation) from the Born in Bradford (BIB) prospective birth cohort in the United Kingdom.

METHODS

In total, 146 fasting serum metabolites, from 2,668 pregnant WE and 2,671 pregnant SA women (average BMI 26.2 kg/m2, average age 27.3 y) were analyzed using partial least squares discriminatory analyses to characterize GDM status. Linear associations between metabolite values and post-oral glucose tolerance test measures of dysglycemia (fasting glucose and 2 h postglucose) were also examined.

RESULTS

Seven metabolites associated with GDM status in both ethnicities (variable importance in projection ≥1), whereas 6 additional metabolites associated with GDM only in WE women. Unique metabolic profiles were observed in healthy-weight women who later developed GDM, with distinct metabolite patterns identified by ethnicity and BMI status. Of the metabolite values analyzed in relation to dysglycemia, lactate, histidine, apolipoprotein A1, HDL cholesterol, and HDL2 cholesterol associated with decreased glucose concentration, whereas DHA and the diameter of very low-density lipoprotein particles (nm) associated with increased glucose concertation in WE women, and in SAs, albumin alone associated with decreased glucose concentration.

CONCLUSIONS

This study shows that the metabolic risk profile for GDM differs between WE and SA women enrolled in BiB in the United Kingdom. This suggests that etiology of the disease differs between ethnic groups and that ethnic-appropriate prevention strategies may be beneficial.

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.

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.

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.

Potential biomarkers identified in plasma of patients with gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is a common complication during pregnancy. Looking for reliable diagnostic markers for early diagnosis can reduce the impact of the disease on the fetus OBJECTIVE: The present study is designed to find plasma metabolites that can be used as potential biomarkers for GDM, and to clarify GDM-related mechanisms METHODS: By non-target metabolomics analysis, compared with their respective controls, the plasma metabolites of GDM pregnant women at 12-16 weeks and 24-28 weeks of pregnancy were analyzed. Multiple reaction monitoring (MRM) analysis was performed to verify the potential marker RESULTS: One hundred and seventy-two (172) and 478 metabolites were identified as differential metabolites in the plasma of GDM pregnant women at 12-16 weeks and 24-28 weeks of pregnancy, respectively. Among these, 40 metabolites were overlapped. Most of them are associated with the mechanism of diabetes, and related to short-term and long-term complications in the perinatal period. Among them, 7 and 10 differential metabolites may serve as potential biomarkers at the 12-16 weeks and 24-28 weeks of pregnancy, respectively. By MRM analysis, compared with controls, increased levels of 17(S)-HDoHE and sebacic acid may serve as early prediction biomarkers of GDM. At 24-28 weeks of pregnancy, elevated levels of 17(S)-HDoHE and L-Serine may be used as auxiliary diagnostic markers for GDM CONCLUSION: Abnormal amino acid metabolism and lipid metabolism in patients with GDM may be related to GDM pathogenesis. Several differential metabolites identified in this study may serve as potential biomarkers for GDM prediction and diagnosis.

Discovery of metabolic biomarkers for gestational diabetes mellitus in a Chinese population

Background

Gestational diabetes mellitus (GDM), one of the most common pregnancy complications, can lead to morbidity and mortality in both the mother and the infant. Metabolomics has provided new insights into the pathology of GDM and systemic analysis of GDM with metabolites is required for providing more clues for GDM diagnosis and mechanism research. This study aims to reveal metabolic differences between normal pregnant women and GDM patients in the second- and third-trimester stages and to confirm the clinical relevance of these new findings.

Methods

Metabolites were quantitated with the serum samples of 200 healthy pregnant women and 200 GDM women in the second trimester, 199 normal controls, and 199 GDM patients in the third trimester. Both function and pathway analyses were applied to explore biological roles involved in the two sets of metabolites. Then the trimester stage-specific GDM metabolite biomarkers were identified by combining machine learning approaches, and the logistic regression models were constructed to evaluate predictive efficiency. Finally, the weighted gene co-expression network analysis method was used to further capture the associations between metabolite modules with biomarkers and clinical indices.

Results

This study revealed that 57 differentially expressed metabolites (DEMs) were discovered in the second-trimester group, among which the most significant one was 3-methyl-2-oxovaleric acid. Similarly, 72 DEMs were found in the third-trimester group, and the most significant metabolites were ketoleucine and alpha-ketoisovaleric acid. These DEMs were mainly involved in the metabolism pathway of amino acids, fatty acids and bile acids. The logistic regression models for selected metabolite biomarkers achieved the area under the curve values of 0.807 and 0.81 for the second- and third-trimester groups. Furthermore, significant associations were found between DEMs/biomarkers and GDM-related indices.

Conclusions

Metabolic differences between healthy pregnant women and GDM patients were found. Associations between biomarkers and clinical indices were also investigated, which may provide insights into pathology of GDM.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12986-021-00606-8.

Metabolic signatures in the conversion from gestational diabetes mellitus to postpartum abnormal glucose metabolism: a pilot study in Asian women

We aimed to identify serum metabolites related to abnormal glucose metabolism (AGM) among women with gestational diabetes mellitus (GDM). The study recruited 50 women diagnosed with GDM during mid-late pregnancy and 50 non-GDM matchees in a Singapore birth cohort. At the 5-year post-partum follow-up, we applied an untargeted approach to investigate the profiles of serum metabolites among all participants. We first employed OPLS-DA and logistic regression to discriminate women with and without follow-up AGM, and then applied area under the curve (AUC) to assess the incremental indicative value of metabolic signatures on AGM. We identified 23 candidate metabolites that were associated with postpartum AGM among all participants. We then narrowed down to five metabolites [p-cresol sulfate, linoleic acid, glycocholic acid, lysoPC(16:1) and lysoPC(20:3)] specifically associating with both GDM and postpartum AGM. The combined metabolites in addition to traditional risks showed a higher indicative value in AUC (0.92–0.94 vs. 0.74 of traditional risks and 0.77 of baseline diagnostic biomarkers) and R² (0.67–0.70 vs. 0.25 of traditional risks and 0.32 of baseline diagnostic biomarkers) in terms of AGM indication, compared with the traditional risks model and traditional risks and diagnostic biomarkers combined model. These metabolic signatures significantly increased the AUC value of AGM indication in addition to traditional risks, and might shed light on the pathophysiology underlying the transition from GDM to AGM.

Unique Biomarker Characteristics in Gestational Diabetes Mellitus Identified by LC-MS-Based Metabolic Profiling

BACKGROUND

Gestational diabetes mellitus (GDM) is a type of glucose intolerance disorder that first occurs during women's pregnancy. The main diagnostic method for GDM is based on the midpregnancy oral glucose tolerance test. The rise of metabolomics has expanded the opportunity to better identify early diagnostic biomarkers and explore possible pathogenesis.

METHODS

We collected blood serum from 34 GDM patients and 34 normal controls for a LC-MS-based metabolomics study.

RESULTS

184 metabolites were increased and 86 metabolites were decreased in the positive ion mode, and 65 metabolites were increased and 71 were decreased in the negative ion mode. Also, it was found that the unsaturated fatty acid metabolism was disordered in GDM. Ten metabolites with the most significant differences were selected for follow-up studies. Since the diagnostic specificity and sensitivity of a single differential metabolite are not definitive, we combined these metabolites to prepare a ROC curve. We found a set of metabolite combination with the highest sensitivity and specificity, which included eicosapentaenoic acid, docosahexaenoic acid, docosapentaenoic acid, arachidonic acid, citric acid, α-ketoglutaric acid, and genistein. The area under the curves (AUC) value of those metabolites was 0.984 between the GDM and control group.

CONCLUSIONS

Our results provide a direction for the mechanism of GDM research and demonstrate the feasibility of developing a diagnostic test that can distinguish between GDM and normal controls clearly. Our findings were helpful to develop novel biomarkers for precision or personalized diagnosis for GDM. In addition, we provide a critical insight into the pathological and biological mechanisms for GDM.

Saturated Oxo Fatty Acids (SOFAs): A Previously Unrecognized Class of Endogenous Bioactive Lipids Exhibiting a Cell Growth Inhibitory Activity

The discovery of novel bioactive lipids that promote human health is of great importance. Combining "suspect" and targeted lipidomic liquid chromatography-high-resolution mass spectrometry (LC-HRMS) approaches, a previously unrecognized class of oxidized fatty acids, the saturated oxo fatty acids (SOFAs), which carry the oxo functionality at various positions of the long chain, was identified in human plasma. A library of SOFAs was constructed, applying a simple green photochemical hydroacylation reaction as the key synthetic step. The synthesized SOFAs were studied for their ability to inhibit in vitro the cell growth of three human cancer cell lines. Four oxostearic acids (OSAs) were identified to inhibit the cell growth of human lung carcinoma A549 cells. 6OSA and 7OSA exhibited the highest cell growth inhibitory potency, suppressing the expression of both STAT3 and c-myc, which are critical regulators of cell growth and proliferation. Thus, naturally occurring SOFAs may play a role in the protection of human health.

Plasma metabolites, especially lipid metabolites, are altered in pregnant women with gestational diabetes mellitus

BACKGROUND AND AIMS

Gestational diabetes mellitus (GDM) is a pathological condition of glucose intolerance associated with adverse pregnancy outcomes and increased risk of developing maternal type 2 diabetes later in life. Metabolomics is finding increasing use in the study of GDM. To date, GDM-specific metabolomic changes have not been completely elucidated.

MATERIALS AND METHODS

In this pilot study, metabolomics fingerprinting data, obtained by ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UHPLC/Q-TOF-MS), of 54 healthy pregnant women and 49 patients with GDM at the second and third gestational trimesters were analyzed. Multilevel statistical methods were used to process complex metabolomic data from the retrospective cohorts.

RESULTS

Using univariate analysis (p < 0.05), 41 metabolites were identified as having the most significant differences between these two groups. Lipid metabolites, particularly glycerophospholipids, were the most prevalent class of altered compounds. In addition, metabolites with previously unknown connection to GDM - such as monoacylglycerol, dihydrobiopterin, and 13S-hydroxyoctadecadienoic acid - were identified with strong discriminative power. The main metabolic pathways affected by GDM included glycerophospholipid metabolism, linoleic acid metabolism, and D-arginine and D-ornithine metabolism.

CONCLUSION

Our data provide a comprehensive overview of metabolite changes at different stages of pregnancy, which offers further insights into the pathogenesis of GDM.

Serum Metabolites as an Indicator of Developing Gestational Diabetes Mellitus Later in the Pregnancy: A Prospective Cohort of a Chinese Population

OBJECTIVE

Gestational diabetes mellitus (GDM) is a common metabolic disorder with onset during pregnancy. However, the etiology and pathogenesis of GDM have not been fully elucidated. In this study, we used a metabolomics approach to investigate the relationship between maternal serum metabolites and GDM in early pregnancy.

METHODS

A nested case-control study was performed. To establish an early pregnancy cohort, pregnant women in early pregnancy (10-13⁺⁶ weeks) were recruited. In total, 51 patients with GDM and 51 healthy controls were included. Serum samples were analyzed using an untargeted high-performance liquid chromatography mass spectrometry metabolomics approach. The relationships between metabolites and GDM were analyzed by an orthogonal partial least-squares discriminant analysis. Differential metabolites were evaluated using a KEGG pathway analysis.

RESULTS

A total of 44 differential metabolites were identified between GDM cases and healthy controls during early pregnancy. Of these, 26 significant metabolites were obtained in early pregnancy after false discovery rate (FDR < 0.1) correction. In the GDM group, the levels of L-pyroglutamic acid, L-glutamic acid, phenylacetic acid, pantothenic acid, and xanthine were significantly higher and the levels of 1,5-anhydro-D-glucitol, calcitriol, and 4-oxoproline were significantly lower than those in the control group. These metabolites were involved in multiple metabolic pathways, including those for amino acid, carbohydrate, lipid, energy, nucleotide, cofactor, and vitamin metabolism.

CONCLUSIONS

We identified significant differentially expressed metabolites associated with the risk of GDM, providing insight into the mechanisms underlying GDM in early pregnancy and candidate predictive markers.

Regulation of maternal-fetal metabolic communication

Pregnancy may be the most nutritionally sensitive stage in the life cycle, and improved metabolic health during gestation and early postnatal life can reduce the risk of chronic disease in adulthood. Successful pregnancy requires coordinated metabolic, hormonal, and immunological communication. In this review, maternal-fetal metabolic communication is defined as the bidirectional communication of nutritional status and metabolic demand by various modes including circulating metabolites, endocrine molecules, and other secreted factors. Emphasis is placed on metabolites as a means of maternal-fetal communication by synthesizing findings from studies in humans, non-human primates, domestic animals, rabbits, and rodents. In this review, fetal, placental, and maternal metabolic adaptations are discussed in turn. (1) Fetal macronutrient needs are summarized in terms of the physiological adaptations in place to ensure their proper allocation. (2) Placental metabolite transport and maternal physiological adaptations during gestation, including changes in energy budget, are also discussed. (3) Maternal nutrient limitation and metabolic disorders of pregnancy serve as case studies of the dynamic nature of maternal-fetal metabolic communication. The review concludes with a summary of recent research efforts to identify metabolites, endocrine molecules, and other secreted factors that mediate this communication, with particular emphasis on serum/plasma metabolomics in humans, non-human primates, and rodents. A better understanding of maternal-fetal metabolic communication in health and disease may reveal novel biomarkers and therapeutic targets for metabolic disorders of pregnancy.

Metabolomics and correlation network analyses of core biomarkers in type 2 diabetes

The identification of metabolic pathways and the core metabolites provide novel molecular targets for the prevention and treatment of diseases. Diabetes is often accompanied with multiple metabolic disorders including hyperglycemia and dyslipidemia. Analysis of the variances of plasma metabolites is critical for identifying potential therapeutic targets for diabetes. In the current study, non-diabetic subjects with normal glucose tolerance and diabetics (age 40-60 years; n = 42 per group) were selected and plasma samples were analyzed by GC-MS for various metabolites profiling followed by network analysis. Our study identified 24 differential metabolites that were mainly enriched in protein synthesis, lipid and amino acid metabolism. Furthermore, we applied the correlation network analysis on these differential metabolites in fatty acid and amino acid metabolism and identified glycerol, alanine and serine as the hub metabolites in diabetic group. In addition, we measured the activities of enzymes in gluconeogenesis and amino acid metabolism and found significant higher activities of fructose 1,6-bisphosphatase, pyruvate carboxylase, lactate dehydrogenase, aspartate aminotransferase and alanine aminotransferase in diabetic patients. In contrast, the enzyme activities of glycolysis pathway (e.g., hexokinase, phosphofructokinase and pyruvate kinase) and TCA cycle (e.g., isocitrate dehydrogenase, succinate dehydrogenase, fumarate hydratase and malate dehydrogenase) were reduced in diabetes. Together, our studies showed that the linoleic acid and amino acid metabolism were the most affected metabolic pathways and glycerol, alanine and serine could play critical role in diabetes. The integration of network analysis and metabolic data could provide novel molecular targets or biomarkers for diabetes.

Diabetes Leads to Alterations in Normal Metabolic Transitions of Pregnancy as Revealed by Time-Course Metabolomics

Women with diabetes during pregnancy are at increased risk of poor maternal and neonatal outcomes. Despite this, the effects of pre-gestational (PGDM) or gestational diabetes (GDM) on metabolism during pregnancy are not well understood. In this study, we utilized metabolomics to identify serum metabolic changes in women with and without diabetes during pregnancy and the cord blood at birth. We observed elevations in tricarboxylic acid (TCA) cycle intermediates, carbohydrates, ketones, and lipids, and a decrease in amino acids across gestation in all individuals. In early gestation, PGDM had elevations in branched-chain amino acids and sugars compared to controls, whereas GDM had increased lipids and decreased amino acids during pregnancy. In both GDM and PGDM, carbohydrate and amino acid pathways were altered, but in PGDM, hemoglobin A1c and isoleucine were significantly increased compared to GDM. Cord blood from GDM and PGDM newborns had similar increases in carbohydrates and choline metabolism compared to controls, and these alterations were not maternal in origin. Our results revealed that PGDM and GDM have distinct metabolic changes during pregnancy. A better understanding of diabetic metabolism during pregnancy can assist in improved management and development of therapeutics and help mitigate poor outcomes in both the mother and newborn.

Adipocytokines are not associated with gestational diabetes mellitus but with pregnancy status

AIMS

Adipose tissue-secreted proteins, i.e. adipocytokines, have been identified as potential mediators linking fat mass and adipose tissue dysfunction with impaired glucose homeostasis, alterations in the inflammatory status, and risk of diabetes. The aim of this study was to determine whether seven circulating adipocytokines are associated with gestational diabetes mellitus (GDM) or are altered by metabolic and weight changes during pregnancy itself.

METHODS

A panel of seven adipocytokines (i.e. adiponectin, adipocyte fatty acid-binding protein, chemerin, leptin, Pro-Enkephalin, progranulin, and Pro-Neurotensin) was quantified in serum in a cross-sectional cohort of 222 women with the following three groups matched for age and body mass index: (i) 74 pregnant women with GDM; (ii) 74 pregnant women without GDM; and (iii) 74 non-pregnant and healthy women. A stepwise statistical approach was used by performing pairwise comparisons, principal component analysis (PCA), and partial least square discriminant analysis (PLS-DA).

RESULTS

Five out of seven adipocytokines were dysregulated between pregnant and non-pregnant women, i.e. adiponectin, chemerin, leptin, Pro-Enkephalin, and progranulin. None of the adipocytokines significantly differed between GDM and non-GDM status during pregnancy. The same five adipocytokines clustered in a principal component representing pregnancy-induced effects. Fasting insulin was the most relevant parameter in the discrimination of GDM as compared to pregnant women without GDM, whereas chemerin and adiponectin were most relevant factors to discriminate pregnancy status.

CONCLUSIONS

Pregnancy status but not presence of GDM can be distinguished by the seven investigated adipocytokines in discrimination analyses.

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 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 .

Metabolomics toward personalized medicine

Metabolomics, which is the metabolites profiling in biological matrices, is a key tool for biomarker discovery and personalized medicine and has great potential to elucidate the ultimate product of the genomic processes. Over the last decade, metabolomics studies have identified several relevant biomarkers involved in complex clinical phenotypes using diverse biological systems. Most diseases result in signature metabolic profiles that reflect the sums of external and internal cellular activities. Metabolomics has a major role in clinical practice as it represents >95% of the workload in clinical laboratories worldwide. Many of these metabolites require different analytical platforms, such as Nuclear Magnetic Resonance (NMR), Mass Spectrometry (MS), and Ultra Performance Liquid Chromatography (UPLC), while many clinically relevant metabolites are still not routinely amenable to detection using currently available assays. Combining metabolomics with genomics, transcriptomics, and proteomics studies will result in a significantly improved understanding of the disease mechanisms and the pathophysiology of the target clinical phenotype. This comprehensive approach will represent a major step forward toward providing precision medical care, in which individual is accounted for variability in genes, environment, and personal lifestyle. In this review, we compare and evaluate the metabolomics strategies and studies that focus on the discovery of biomarkers that have "personalized" diagnostic, prognostic, and therapeutic value, validated for monitoring disease progression and responses to various management regimens.

Alterations of Sphingolipid Metabolism in Different Types of Polycystic Ovary Syndrome

The roles of sphingolipids in polycystic ovary syndrome (PCOS) are still unknown. This study aimed to investigate the sphingolipid characteristics for different types of PCOS using liquid chromatography-mass spectrometry (LC-MS). A total of 107 women with PCOS and 37 healthy women as normal controls were studied. PCOS patients were further classified into non-obesity with insulin resistance (IR) (NOIR), obesity with IR (OIR), and non-obesity and non-IR (NIR) subgroups. A total of 87 serum sphingolipids, including 9 sphingosines, 3 sphinganines, 1 sphingosine-1-phosphate (S1P), 19 ceramides (Cers), 1 ceramide-1-phosphate, 44 sphingomyelins (SMs), 4 hexosylceramides, and 6 lactosylceramides (LacCers) were analyzed using an improved sphingolipidomic approach based on LC-MS. Notable elevations in the levels of S1P, Cer, and SM were observed in PCOS patients when compared with healthy women, and SM species with long saturated acyl chains showed potential as novel biomarkers of PCOS. In addition, the level of LacCer was only elevated in NIR, and there was almost no change in NOIR and OIR. This study is the first to report the comprehensive sphingolipidomic profiling of different subgroups of PCOS with or without IR or obesity and suggests that serum sphingolipids might be useful as diagnostic biomarkers for different types of PCOS.

[High-performance liquid chromatography-mass spectrometry-based serum metabolic profiling in patients with HBV-related hepatocellular carcinoma]

OBJECTIVE

To explore the diagnostic value of the serum metabolites identified by high-performance liquid chromatography-mass spectrometry (HPLC/MS) for hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC).

METHODS

A total of 126 patients admitted to Tianjin Third Central Hospital were enrolled, including 27 patients with HBV-related hepatitis with negative viral DNA (DNA-N), 24 with HBV-related hepatitis with positive viral DNA, 24 with HBV-related liver cirrhosis, 27 with HBV-related HCC undergoing surgeries or radiofrequency ablation, and 24 with HBV-related HCC receiving interventional therapy, with 25 healthy volunteers as the normal control group. Serum samples were collected from all the subjects for HPLC/MS analysis, and the data were pretreated to establish an orthogonal partial least- squares discriminant analysis (OPLS-DA) model. The differential serum metabolites were preliminarily screened by comparisons between the HBV groups and the control group, and the characteristic metabolites were identified according to the results of non-parametric test. The potential clinical values of these characteristic metabolites were evaluated using receiver operator characteristic curve (ROC) analysis.

RESULTS

A total of 25 characteristic metabolites were identified in the HBV- infected patients, including 9 lysophosphatidylcholines, 2 fatty acids, 17α-estradiol, sphinganine, 5-methylcytidine, vitamin K2, lysophosphatidic acid, glycocholic acid and 8 metabolites with few reports. The patients with HBV- related HCC showed 22 differential serum metabolites compared with the control group, 4 differential metabolites compared with patients with HBV-related liver cirrhosis; 10 differential metabolites were identified in patients with HBV-related HCC receiving interventional therapy compared with those receiving surgical resection or radiofrequency ablation. From the normal control group to HBV-related HCC treated by interventional therapy, many metabolites underwent variations following a similar pattern.

CONCLUSIONS

We identified 25 characteristic metabolites in patients with HBV-related HCC, and these metabolites may have potential clinical values in the diagnosis of HBV-related HCC. The continuous change of some of these metabolites may indicate the possibility of tumorigenesis, and some may also have indications for the choice of surgical approach.

The role of mass spectrometry in studies of glycation processes and diabetes management

In the last decade, mass spectrometry has been widely employed in the study of diabetes. This was mainly due to the development of new, highly sensitive, and specific methods representing powerful tools to go deep into the biochemical and pathogenetic processes typical of the disease. The aim of this review is to give a panorama of the scientifically valid results obtained in this contest. The recent studies on glycation processes, in particular those devoted to the mechanism of production and to the reactivity of advanced glycation end products (AGEs, AGE peptides, glyoxal, methylglyoxal, dicarbonyl compounds) allowed to obtain a different view on short and long term complications of diabetes. These results have been employed in the research of effective markers and mass spectrometry represented a precious tool allowing the monitoring of diabetic nephropathy, cardiovascular complications, and gestational diabetes. The same approaches have been employed to monitor the non-insulinic diabetes pharmacological treatments, as well as in the discovery and characterization of antidiabetic agents from natural products. © 2018 Wiley Periodicals, Inc. Mass Spec Rev 38:112-146, 2019.

Metabolomics of Pregnancy Complications: Emerging Application of Maternal Hair

In recent years, the study of metabolomics has begun to receive increasing international attention, especially as it pertains to medical research. This is due in part to the potential for discovery of new biomarkers in the metabolome and to a new understanding of the "exposome", which refers to the endogenous and exogenous compounds that reflect external exposures. Consequently, metabolomics research into pregnancy-related issues has increased. Biomarkers discovered through metabolomics may shed some light on the etiology of certain pregnancy-related complications and their adverse effects on future maternal health and infant development and improve current clinical management. The discoveries and methods used in these studies will be compiled and summarized within the following paper. A further focus of this paper is the use of hair as a biological sample, which is gaining increasing attention across diverse fields due to its noninvasive sampling method and the metabolome stability. Its significance in exposome studies will be considered in this review, as well as the potential to associate exposures with adverse pregnancy outcomes. Currently, hair has been used in only two metabolomics studies relating to fetal growth restriction (FGR) and gestational diabetes mellitus (GDM).

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.

Metabolomic profiling of women with gestational diabetes mellitus and their offspring: Review of metabolomics studies

BACKGROUND

Gestational diabetes mellitus (GDM) reflects an increased risk of developing type 2 diabetes (T2D) after pregnancy in women. Offspring born to mothers with GDM are at an elevated risk of obesity and T2D at a young age. Currently, there are lack of ways for identifying women in early pregnancy who are at risk of developing GDM. As a result, both mothers and fetus are not treated until late in the second trimester when GDM is diagnosed. The recent advance in metabolomics, a new approach of systematic investigation of the metabolites, provides an opportunity for early detection of GDM, and classifying the risk of subsequent chronic diseases among women and their offspring.

METHODS

We reviewed the literatures published in the past 20 years on studies using high-throughput metabolomics technologies to investigate women with GDM and their offspring.

CONCLUSIONS

Despite the inconsistent results, previous studies have identified biomarkers that involved in specific metabolite groups and several pathways, including amino acid metabolism, steroid hormone biosynthesis, glycerophospholipid metabolism, and fatty acid metabolism. However, most studies have small sample sizes. Further research is warranted to determine if metabolomics will result in new indicators for the diagnosis, management, and prognosis of GDM and related complications.

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.

Potential Impact of Metabolic and Gut Microbial Response to Pregnancy and Lactation in Lean and Diet-Induced Obese Rats on Offspring Obesity Risk

SCOPE

Maternal obesity programs metabolic dysfunction in offspring, increasing their susceptibility to obesity and metabolic diseases in later life. Moreover, pregnancy and lactation are associated with many metabolic adaptations, yet it is unclear how diet-induced maternal obesity may interrupt these processes.

METHODS AND RESULTS

¹ H NMR serum metabolomics analysis was performed on samples collected pre-pregnancy and in pregnant and lactating lean and high fat/sucrose (HFS) diet-induced obese Sprague-Dawley rats to identify maternal metabolic pathways associated with developmental programming of offspring obesity. Gut microbial composition was assessed using qPCR. Offspring of HFS dams had nearly 40% higher adiposity at weaning compared to offspring of lean dams. While pregnancy and lactation were associated with distinct maternal metabolic changes common to both lean and obese dams, we identified several metabolic differences, potentially implicating dysregulated one-carbon and mammary gland metabolism in the metabolic programming of obesity. Gut microbial composition was significantly altered with obesity, and both gestation and lactation were accompanied by changes in gut microbiota.

CONCLUSION

Diet-induced maternal obesity and consumption of an obesogenic maternal diet results in differential metabolic and gut microbial adaptations to pregnancy and lactation; these maladaptations may be directly involved in maternal programming of offspring susceptibility to obesity.

Gestational diabetes mellitus: an updated overview

The clinical and public health relevance of gestational diabetes mellitus (GDM) is widely debated due to its increasing incidence, the resulting negative economic impact, and the potential for severe GDM-related pregnancy complications. Also, effective prevention strategies in this area are still lacking, and controversies exist regarding diagnosis and management of this form of diabetes. Different diagnostic criteria are currently adopted worldwide, while recommendations for diet, physical activity, healthy weight, and use of oral hypoglycemic drugs are not always uniform. In the present review, we provide an update of current insights on clinical aspects of GDM, by discussing the more controversial issues.

Metabolic Biomarkers for Prognostic Prediction of Pre-diabetes: results from a longitudinal cohort study

To investigate the metabolic biomarkers of predicting the transition from pre-diabetes (pre-DM) to normal glucose regulation (NGR) and diabetes (DM) in a longitudinal cohort study. 108 participants with pre-DM were followed up for ten years and divided into 3 groups according to different glycemic outcomes. 20 participants progressed to DM, 20 regressed to NGR, and 68 remained at pre-DM. Alterations in plasma metabolites in these groups were evaluated by untargeted ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS). Twenty three metabolites related to glycerophospholipid metabolism, oxidation and antioxidation were associated with the process from pre-DM to NGR, while twenty two metabolites related to amino acid metabolism, glycerophospholipid metabolism and mitochondrial β-oxidation played important roles in the progression to DM. Results from stepwise logistic regression analysis showed that five biomarkers (20-Hydroxy-leukotriene E4, Lysopc(20:4), 5-methoxytryptamine, Endomorphin-1, Lysopc(20:3)) were good prediction for the restoration to NGR, and five biomarkers (Iso-valeraldehyde, linoleic acid, Lysopc(18:1), 2-Pyrroloylglycine, Dityrosine) for the development of DM. The findings suggest that the combination of these potential metabolites may be used for the prognosis of pre-DM. Targeting the pathways that involved in these prognostic biomarkers would be beneficial for the regression to NGR and the early prevention of DM among pre-DM.

Tryptophan and purine metabolites are consistently upregulated in the urinary metabolome of patients diagnosed with gestational diabetes mellitus throughout pregnancy: A longitudinal metabolomics study of Chinese pregnant women part 2

BACKGROUND

Gestational diabetes mellitus (GDM) is a pathological state of glucose intolerance associated with adverse pregnancy outcomes and an increased risk of developing maternal type 2 diabetes later in life. The mechanisms underlying GDM development are not fully understood. We examined the pathophysiology of GDM through comprehensive metabolic profiling of maternal urine, using participants from a longitudinal cohort of normal pregnancies and pregnancies complicated by GDM.

METHODS

Based on ultra-performance liquid chromatography/hybrid quadrupole time-of-flight mass spectrometry, an untargeted metabolomics study was performed to explore the differences in the urinary metabolome of GDM cases and healthy controls over the course of pregnancy. Multilevel statistical approaches were employed to address the complex metabolomic data obtained from a longitudinal cohort.

RESULTS

The results indicated that tryptophan and purine metabolism was associated with GDM. The tryptophan-kynurenine pathway was activated in the GDM subjects before placental hormones or the fetoplacental unit could have produced any physiological effect. Hypoxanthine, xanthine, xanthosine, and 1-methylhypoxanthine were all elevated in the urine metabolome of subjects with GDM. Catabolism of purine nucleosides leads ultimately to the production of uric acid, which discriminated the subjects with GDM from controls.

CONCLUSIONS

The results support the notion that GDM may be a predisposed condition, or prediabetic state, which is manifested during pregnancy. This challenges the conventional view of the pathogenesis of GDM, which assumes placental hormones are the major causes of insulin resistance in GDM.

Metabolomics biomarker analysis of threatened abortion in polycystic ovary syndrome: a clinical discovery study

In this study, using an advanced metabolomics platform based on UPLC-QTOF-MS, we found that pregnancy significantly altered the profile of metabolites in the plasma of women with PCOS.

The role of amino acid profiles in diabetes risk assessment

PURPOSE OF REVIEW

The concentrations of plasma-free amino acids, such as branched-chain amino acids and aromatic amino acids, are associated with visceral obesity, insulin resistance, and the future development of diabetes and cardiovascular diseases. This review discusses recent progress in the early assessment of the risk of developing diabetes and the reversal of altered plasma-free amino acids through interventions. Additionally, recent developments that have increased the utility of amino acid profiling technology are also described.

RECENT FINDINGS

Plasma-free amino acid alterations in the early stage of lifestyle-related diseases are because of obesity and insulin resistance-related inflammation, and these alterations are reversed by appropriate (nutritional, drug, or surgical) interventions that improve insulin sensitivity. For clinical applications, procedures for measuring amino acids are being standardized and automated.

SUMMARY

Plasma-free amino acid profiles have potential as biomarkers for both assessing diabetes risk and monitoring the effects of strategies designed to lower that risk. In addition, the methodology for measuring amino acids has been refined, with the goal of routine clinical application.

Association between serum bile acid profiles and gestational diabetes mellitus: A targeted metabolomics study

BACKGROUND

Given the potential influence of aberrant bile acid metabolism on glucose homeostasis, we hypothesized that serum bile acid metabolism is altered in gestational diabetes mellitus (GDM). We characterized the metabolic profiling changes of serum bile acids in GDM and to find the potential biomarkers for the diagnosis and differential diagnosis of GDM.

METHODS

Based on ultrahigh performance liquid chromatography/hybrid quadrupole time-of-flight mass spectrometry, a targeted metabolomics study that involved targeted and untargeted screening techniques was performed to explore the changes in serum bile acid metabolism of GDM cases, intrahepatic cholestasis of pregnancy (ICP) cases and healthy controls.

RESULTS

There were 3 significantly different profiling of serum bile acids for GDM, ICP and controls. Compared to the controls, GDM individuals demonstrated significant increases in 8 bile acid species, including 2 dihydroxy conjugated, 1 trihydroxy unconjugated and 5 sulfated bile acids. β-muricholic acid (β-MCA) and di-2 were well-suited to use as the metabolic markers for the diagnosis and differential diagnosis of GDM, respectively.

CONCLUSIONS

These preliminary findings revealed the protective effect of body against cytotoxicity via elimination of increased sulfated bile acids and aberrant enzyme activity participated in the cycle β-MCA→hyodeoxycholic acid (HDCA) of the bile acid metabolism pathway for the women with GDM, which gave us further insights into the etiology and pathophysiology of GDM.