Metabolomics of Diabetes in Pregnancy

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.

Myeloid-derived suppressor cells: Are they involved in gestational diabetes mellitus?

Gestational diabetes mellitus (GDM) is currently the most common metabolic complication during pregnancy, with an increasing prevalence worldwide. Maternal immune dysregulation might be partly responsible for the pathophysiology of GDM. Myeloid derived suppressor cells (MDSCs) are a heterogeneous population of cells, emerging as a new immune regulator with potent immunosuppressive capacity. Although the fate and function of these cells were primarily described in pathological conditions such as cancer and infection, accumulating evidences have spotlighted their beneficial roles in homeostasis and physiological conditions. Recently, several studies have explored the roles of MDSCs in the diabetic microenvironment. However, the fate and function of these cells in GDM are still unknown. The current review summarized the existing knowledges about MDSCs and their potential roles in diabetes during pregnancy in an attempt to highlight our current understanding of GDM-related immune dysregulation and identify areas where further study is required.

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.

Preventive effect of one-day outpatient health management on adverse pregnancy outcomes in patients with gestational diabetes mellitus: a retrospective cohort study

BACKGROUND

One-day outpatient health management has been applied to treat gestational diabetes mellitus (GDM) and prevent further complications. However, the relationships between one-day outpatient health management and adverse pregnancy outcomes remain ambiguous, because of limited evidence. We analyzed the effects of one-day outpatient health management on premature birth, macrosomia and low-birth-weight infants in patients with GDM.

METHODS

We retrospectively enrolled pregnant women with GDM who delivered at Guiyang Maternal and Child Health Hospital between 2019 and 2021. Patients could voluntarily choose to participate in either the general outpatient health education or a one-day outpatient health management. Data on demographic and clinical characteristics were collected and pregnancy outcomes ascertained. Logistic regression analysis was used to detect the potential relationship between one-day outpatient health management and adverse pregnancy outcomes including preterm birth, macrosomia, and low-birth-weight infants. GDM, preterm birth, low birth weight and macrosomia was diagnosed according to the criteria established by Obstetrics and Gynecology (9^th edition).

RESULTS

A total of 3,249 patients with GDM were included, and 798 (24.56%) patients participated in the one-day outpatient health management. Statistically significant differences were observed in the maternal age (P<0.05) and gravidity (P<0.001) between the study and control groups. The incidences of premature birth, low-birth-weight infant, and macrosomia in patients attending the one-day outpatient service were 9.6%, 8.1%, and 4.5%, while the incidences of those who did not attend the one-day outpatient service were 12.4%, 11.1%, and 7.5%. After adjusting for maternal age, ethnic groups, body mass index (BMI) before pregnancy, family history of diabetes, history of abnormal pregnancy, history of polycystic ovary syndrome, gravidity, hyperthyroidism and hypothyroidism, multivariate logistic regression analyses showed that this one-day outpatient health management was a protective factor for premature birth [odds ratio (OR) 0.751, 95% confidence interval (CI): 0.576-0.981], macrosomia (OR 0.567, 95% CI: 0.385-0.834) and low-birth-weight infants (OR 0.699, 95% CI: 0.522-0.937).

CONCLUSIONS

The degree of acceptance of patients with GDM to a one-day outpatient health management is still low. This one-day outpatient health management may reduce the incidence of adverse pregnancy outcomes in women with GDM to a certain extent.

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.

miR-199a Is Upregulated in GDM Targeting the MeCP2-Trpc3 Pathway

Gestational diabetes mellitus (GDM), the most common medical pregnancy complication, has become a growing problem. More and more studies have shown that microRNAs are closely related to metabolic processes. The purpose of this paper is to investigate the role of up-regulation of miR-199a-5p expression in GDM. We found that miR-199a-5p was significantly up-regulated in the placenta of GDM patients compared with normal pregnant women, and expressed in placental villi. miR-199a-5p can regulate the glucose pathway by inhibiting the expression of methyl CpG-binding protein 2 (MeCP2) and down-regulating canonical transient receptor potential 3 (Trpc3). This suggests that miR-199a-5p may regulate the glucose pathway by regulating methylation levels, leading to the occurrence of GDM.

Plasma lipidomics in early pregnancy and risk of gestational diabetes mellitus: a prospective nested case-control study in Chinese women

BACKGROUND

Lipid metabolism plays an important role in the pathogenesis of diabetes. There is little evidence regarding the prospective association of the maternal lipidome with gestational diabetes mellitus (GDM), especially in Chinese populations.

OBJECTIVES

We aimed to identify novel lipid species associated with GDM risk in Chinese women, and assess the incremental predictive capacity of the lipids for GDM.

METHODS

We conducted a nested case-control study using the Tongji-Shuangliu Birth Cohort with 336 GDM cases and 672 controls, 1:2 matched on age and week of gestation. Maternal blood samples were collected at 6-15 wk, and lipidomes were profiled by targeted ultra-HPLC-tandem MS. GDM was diagnosed by oral-glucose-tolerance test at 24-28 wk. The least absolute shrinkage and selection operator is a regression analysis method that was used to select novel biomarkers. Multivariable conditional logistic regression was used to estimate the associations.

RESULTS

Of 366 detected lipids, 10 were selected and found to be significantly associated with GDM independently of confounders: there were positive associations with phosphatidylinositol 40:6, alkylphosphatidylcholine 36:1, phosphatidylethanolamine plasmalogen 38:6, diacylglyceride 18:0/18:1, and alkylphosphatidylethanolamine 40:5 (adjusted ORs per 1 log-SD increment range: 1.34-2.86), whereas there were inverse associations with sphingomyelin 34:1, dihexosyl ceramide 24:0, mono hexosyl ceramide 18:0, dihexosyl ceramide 24:1, and phosphatidylcholine 40:7 (adjusted ORs range: 0.48-0.68). Addition of these novel lipids to the classical GDM prediction model resulted in a significant improvement in the C-statistic (discriminatory power of the model) to 0.801 (95% CI: 0.772, 0.829). For every 1-point increase in the lipid risk score of the 10 lipids, the OR of GDM was 1.66 (95% CI: 1.50, 1.85). Mediation analysis suggested the associations between specific lipid species and GDM were partially explained by glycemic and insulin-related indicators.

CONCLUSIONS

Specific plasma lipid biomarkers in early pregnancy were associated with GDM in Chinese women, and significantly improved the prediction for GDM.

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

CONTEXT

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

MicroRNA-362-5p promotes the proliferation and inhibits apoptosis of trophoblast cells via targeting glutathione-disulfide reductase

Gestational diabetes mellitus (GDM), a common complication of pregnancy, harms the health of pregnant women and fetuses. MicroRNAs (miRNAs) dysregulation in placenta is involved in GDM. Herein, we explored the roles of miR-362-5p in GDM. After high glucose (HG) treated HTR-8/SVneo cells, CCK-8 and flow cytometry were conducted to assess the capability of the proliferation and apoptosis, respectively. The data demonstrated that HG inhibited proliferation and induced apoptosis of HTR-8/SVneo cells. MiR-362-5p level was reduced in HG-treated cells and placenta tissues of GDM patients, measured by qPCR. Overexpressed miR-362-5p accelerated the proliferation and restrained apoptosis of HG-treated cells. Furthermore, glutathione-disulfide reductase (GSR) was verified as a target of miR-362-5p, through TargetScan database and dual-luciferase reporter assay. GSR was upregulated in GDM placenta tissues and was negatively regulated by miR-362-5p. Enforced GSR level abolished the effects of miR-362-5p overexpression on the proliferation and apoptosis of HTR-8/SVneo cells. Furthermore, miR-362-5p increased p-PI3K, p-AKT and bcl-2, while reduced bax and cleaved caspase3, which were abolished by GSR. In conclusion, miR-362-5p promoted cell proliferation and inhibited apoptosis via targeting GSR and activating PI3K/AKT pathway. The findings mentioned above suggested that miR-362-5p might be a therapy target of GDM.

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.

A mid-pregnancy risk prediction model for gestational diabetes mellitus based on the maternal status in combination with ultrasound and serological findings

Although previous studies have proposed predictive models of gestational diabetes mellitus (GDM) based on maternal status, they do not always provide reliable results. The present study aimed to create a novel model that included ultrasound data of maternal fat distribution and serum inflammatory factors. The clinical data of 1,158 pregnant women treated at Tangshan Gongren Hospital and eight other flagship hospitals in Tangshan, including the First Hospital of Tangshan Gongren Hospital group, Ninth Hospital of Tangshan Gongren Hospital group, Tangshan Gongren Hospital group rehabilitation hospital, Tangshan railway central hospital, Tangshan Gongren Hospital group Fengnan hospital, Tangshan Gongren Hospital group Qianan Yanshan hospital, Tangshan Gongren Hospital group Qianxi Kangli hospital and Tangshan Gongren Hospital group Jidong Sub-hospital, were analyzed following the division of subjects into GDM and non-GDM groups according to their diagnostic results at 24-28 weeks of pregnancy. Univariate analysis was performed to investigate the significance of the maternal clinical parameters for GDM diagnosis and a GDM prediction model was established using stepwise regression analysis. The predictive value of the model was evaluated using a Homer-Lemeshow goodness-of-fit test and a receiver operating characteristic curve (ROC). The model demonstrated that age, pre-pregnancy body mass index, a family history of diabetes mellitus, polycystic ovary syndrome, a history of GDM, high systolic pressures, glycosylated hemoglobin levels, triglyceride levels, total cholesterol levels, low-density lipoprotein cholesterol levels, serum hypersensitive C-reactive protein, increased subcutaneous fat thickness and visceral fat thickness were all correlated with an increased GDM risk (all P<0.01). The area under the curve value was 0.911 (95% CI, 0.893-0.930). Overall, the results indicated that the current model, which included ultrasound and serological data, may be a more effective predictor of GDM compared with other single predictor models. In conclusion, the present study developed a tool to determine the risk of GDM in pregnant women during the second trimester. This prediction model, based on various risk factors, demonstrated a high predictive value for the GDM occurrence in pregnant women in China and may prove useful in guiding future clinical practice.

How could metabolomics change pediatric health?

In the last years, 'omics' technologies, and especially metabolomics, emerged as expanding scientific disciplines and promising technologies in the characterization of several pathophysiological processes.In detail, metabolomics, able to detect in a dynamic way the whole set of molecules of low molecular weight in cells, tissues, organs, and biological fluids, can provide a detailed phenotypic portray, representing a metabolic "snapshot."Thanks to its numerous strength points, metabolomics could become a fundamental tool in human health, allowing the exact evaluation of individual metabolic responses to pathophysiological stimuli including drugs, environmental changes, lifestyle, a great number of diseases and other epigenetics factors.Moreover, if current metabolomics data will be confirmed on larger samples, such technology could become useful in the early diagnosis of diseases, maybe even before the clinical onset, allowing a clinical monitoring of disease progression and helping in performing the best therapeutic approach, potentially predicting the therapy response and avoiding overtreatments. Moreover, the application of metabolomics in nutrition could provide significant information on the best nutrition regimen, optimal infantile growth and even in the characterization and improvement of commercial products' composition.These are only some of the fields in which metabolomics was applied, in the perspective of a precision-based, personalized care of human health.In this review, we discuss the available literature on such topic and provide some evidence regarding clinical application of metabolomics in heart diseases, auditory disturbance, nephrouropathies, adult and pediatric cancer, obstetrics, perinatal conditions like asphyxia, neonatal nutrition, neonatal sepsis and even some neuropsychiatric disorders, including autism.Our research group has been interested in metabolomics since several years, performing a wide spectrum of experimental and clinical studies, including the first metabolomics analysis of human breast milk. In the future, it is reasonable to predict that the current knowledge could be applied in daily clinical practice, and that sensible metabolomics biomarkers could be easily detected through cheap and accurate sticks, evaluating biofluids at the patient's bed, improving diagnosis, management and prognosis of sick patients and allowing a personalized medicine. A dream? May be I am a dreamer, but I am not the only one.

Cardiac structure and function in youth with type 2 diabetes in the iCARE cohort study: Cross-sectional associations with prenatal exposure to diabetes and metabolomic profiles

OBJECTIVE

This study aimed to determine the degree of left ventricular (LV) dysfunction and its determinants in adolescents with type 2 diabetes (T2D). We hypothesized that adolescents with T2D would display impaired LV diastolic function and that these cardiovascular complications would be exacerbated in youth exposed to maternal diabetes in utero.

METHODS

Left ventricular structure and function, carotid artery intima media thickness and strain, and serum metabolomic profiles were compared between adolescents with T2D (n = 121) and controls (n = 34). Sub-group analyses examined the role of exposure to maternal diabetes as a determinant of LV or carotid artery structure and function among adolescents with T2D.

RESULTS

Adolescents with T2D were 15.1 ± 2.5 years old, (65% female, 99% Indigenous), had lived with diabetes for 2.7 ± 2.2 years, had suboptimal glycemic control (HbA1c = 9.4 ± 2.6%) and 58% (n = 69) were exposed to diabetes in utero. Compared to controls, adolescents with T2D displayed lower LV diastolic filling (early diastole/atrial filling rate ratio [E/A] = 1.9 ± 0.6 vs 2.2 ± 0.6, P = 0.012), lower LV relaxation and carotid strain (0.12 ± 0.05 vs 0.17 ± 0.05, P = .03) and elevated levels of leucine, isoleucine and valine. Among adolescents with T2D, exposure to diabetes in utero was not associated with differences in LV diastolic filling, LV relaxation, carotid strain or branched chain amino acids.

CONCLUSIONS

Adolescents with T2D display LV diastolic dysfunction, carotid artery stiffness, and elevated levels of select branch chain amino acids; differences were not associated with exposure to maternal diabetes in utero.

Hyperglycaemia and factors associated with dental caries in immediate postpartum women

Objective: Dental caries and hyperglycaemia share common risk factors. The aim of this study was to identify factors associated with dental caries in women in the immediate postpartum period. It also verified whether women with hyperglycaemia presented more dental caries than those with normal glycaemia.Material and Methods: This cross-sectional study was performed on 297 women recruited from a teaching hospital in Brazil (from October 2011 to November 2012). Dental caries and oral biofilm were evaluated by oral examination. The blood glucose was accessed by Haemoglobin A1c test. Information on socioeconomic characteristics, harmful habits and oral health habits was also gathered.Results: More than half (66%) of the women had carious lesions. Univariate analysis showed no association between hyperglycaemia and dental caries (p = .39). The hierarchical logistic regression model showed that the following variables were associated with dental caries: maternal education level ≤8 years (ORadjusted = 2.40 [CI 1.19-4.82]), previous children (OR_adjusted = 1.81 [CI 1.08-3.03), use of dental floss (OR_adjusted = .48 [CI 0.27-0.86]), and visible plaque index ≥30% (OR_adjusted = 1.83 [CI 1.05-3.20]).Conclusions: These findings call attention to the need to implement effective public policies directed at avoiding tooth decay in pregnancy and in the postpartum period.

Metabolic Profiling in Blastocoel Fluid and Blood Plasma of Diabetic Rabbits

Metabolic disorders of the mother adversely affect early embryo development, causing changes in maternal metabolism and consequent alterations in the embryo environment in the uterus. The goal of this study was to analyse the biochemical profiles of embryonic fluids and blood plasma of rabbits with and without insulin-dependent diabetes mellitus (DT1), to identify metabolic changes associated with maternal diabetes mellitus in early pregnancy. Insulin-dependent diabetes was induced by alloxan treatment in female rabbits 10 days before mating. On day 6 post-coitum, plasma and blastocoel fluid (BF) were analysed by ultrahigh performance liquid chromatography-tandem mass spectroscopy (UPLC-MS/MS) (Metabolon Inc. Durham, NC, USA). Metabolic datasets comprised a total of 284 and 597 compounds of known identity in BF and plasma, respectively. Diabetes mellitus had profound effects on maternal and embryonic metabolic profiles, with almost half of the metabolites changed. As predicted, we observed an increase in glucose and a decrease in 1,5-anhydroglucitol in diabetic plasma samples. In plasma, fructose, mannose, and sorbitol were elevated in the diabetic group, which may be a way of dealing with excess glucose. In BF, metabolites of the pentose metabolism were especially increased, indicating the need for ribose-based compounds relevant to DNA and RNA metabolism at this very early stage of embryo development. Other changes were more consistent between BF and plasma. Both displayed elevated acylcarnitines, body3-hydroxybutyrate, and multiple compounds within the branched chain amino acid metabolism pathway, suggesting that lipid beta-oxidation is occurring at elevated levels in the diabetic group. This study demonstrates that maternal and embryonic metabolism are closely related. Maternal diabetes mellitus profoundly alters the metabolic profile of the preimplantation embryo with changes in all subclasses of metabolites.

Nomogram for prediction of gestational diabetes mellitus in urban, Chinese, pregnant women

Background

This study sought to develop and validate a nomogram for prediction of gestational diabetes mellitus (GDM) in an urban, Chinese, antenatal population.

Methods

Age, pre-pregnancy body mass index (BMI), fasting plasma glucose (FPG) in the first trimester and diabetes in first degree relatives were incorporated as validated risk factors. A prediction model (nomogram) for GDM was developed using multiple logistic regression analysis, from a retrospective study conducted on 3956 women who underwent their first antenatal visit during 2015 in Shanghai. Performance of the nomogram was assessed through discrimination and calibration. We refined the predicting model with t-distributed stochastic neighbor embedding (t-SNE) to distinguish GDM from non-GDM. The results were validated using bootstrap resampling and a prospective cohort of 6572 women during 2016 at the same institution.

Results

Advanced age, pre-pregnancy BMI, high first-trimester, fasting, plasma glucose, and, a family history of diabetes were positively correlated with the development of GDM. This model had an area under the receiver operating characteristic (ROC) curve of 0.69 [95% CI:0.67–0.72, p < 0.0001]. The calibration curve for probability of GDM showed good consistency between nomogram prediction and actual observation. In the validation cohort, the ROC curve was 0.70 [95% CI: 0.68–0.72, p < 0.0001] and the calibration plot was well calibrated. In exploratory and validation cohorts, the distinct regions of GDM and non-GDM were distinctly separated in the t-SNE, generating transitional boundaries in the image by color difference. Decision curve analysis showed that the model had a positive net benefit at threshold between 0.05 and 0.78.

Conclusions

This study demonstrates the ability of our model to predict the development of GDM in women, during early stage of pregnancy.

MTNR1B genetic polymorphisms as risk factors for gestational diabetes mellitus: a case-control study in a single tertiary care center

BACKGROUND

Gestational diabetes mellitus (GDM) is a metabolic disease in pregnancy that causes carbohydrate intolerance and hyper-glycemia. Genome-wide association studies and meta-analyses have found that the single nucleotide polymorphisms (SNPs) rs1387153 and rs10830963 of the melatonin receptor 1B ( MTNR1B) gene are associated with GDM. No studies on the MTNR1B gene effect on GDM have been performed in Saudis, other Arabs, or other Middle Eastern populations.

OBJECTIVES

Investigate the association of genotype or allele frequencies of the two SNPs with GDM and with clinical parameters related to GDM.

DESIGN

Case-control study.

SETTINGS

Tertiary care center, Riyadh.

PATIENTS AND METHODS

We recruited 400 pregnant Saudi women ages 18-45 years (200 were diagnosed with GDM, and 200 were healthy controls). Biochemical assays were performed, and rs1387153 and rs10830963 polymorphisms were analyzed by polymerase chain reaction-restriction fragment length polymorphism analysis and real-time polymerase chain reaction with TaqMan genotyping.

MAIN OUTCOME MEASURES

The association of MTNR1B gene (rs1387153 and rs10830963 polymorphisms) with GDM and with biochemical parameters related to GDM.

SAMPLE SIZE

200 GDM cases and 200 non-GDM controls.

RESULTS

Differences in allele frequencies for GDM vs non-GMD were statistically significant or nearly significant for both SNPs after adjustment for age and body mass index. In a logistic regression analysis, genotype TT was positively associated with post-prandial blood glucose (P=.018), but other associations were not statistically significant.

CONCLUSION

The odds ratios for the associations between the rs1387153 and rs10830963 SNPs and GDM exceeded 1.5-fold, which is higher than typically reported for diseases with complex genetic background. These effect sizes for GDM suggest pregnancy-specific factors related to the MTNR1B risk genotypes.

LIMITATIONS

Only two SNPs were studied.

CONFLICT OF INTEREST

None.

Exploration of predictive metabolic factors for gestational diabetes mellitus in Japanese women using metabolomic analysis

AIMS/INTRODUCTION

We aimed to explore novel predictive markers for gestational diabetes mellitus using metabolomic analysis in pregnant Japanese women.

MATERIALS AND METHODS

We carried out a case-control study with a cohort of participants enrolled during the first or early second trimester in the Center of Chiba Unit of the Japan Environment and Children's Study. Participants were classified as either gestational diabetes mellitus cases or matched controls based on age, body mass index and parity. Metabolite levels of their serum and urine obtained randomly before the diagnosis of gestational diabetes mellitus were analyzed using hydrophilic interaction chromatography tandem mass spectrometry. Orthogonal projections to latent structures discriminant analysis was carried out to investigate metabolome profiles for the different groups. Metabolites with a variable importance in projection value of >1.5 were identified as potential markers.

RESULTS

In total, 242 participants were enrolled in the study, of which 121 were cases. The R2X, R2Y and Q2 parameters for the discrimination ability of the resulting models were 0.388, 0.492 and 0.45 for serum, and 0.454, 0.674 and 0.483 for urine, respectively. We finally identified three metabolites in serum and 20 in urine as potential biomarkers. Glutamine in serum and ethanolamine and 1,3-diphosphoglycerate in urine showed >0.8 area under the receiver operating characteristic curves.

CONCLUSIONS

The present study identified serum and urine metabolites that are possible predictive markers of subsequent gestational diabetes mellitus in Japanese women. Further studies are required to elucidate their efficacy.

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.

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.

Association of Gestational Diabetes With Maternal Disorders of Glucose Metabolism and Childhood Adiposity

IMPORTANCE

The sequelae of gestational diabetes (GD) by contemporary criteria that diagnose approximately twice as many women as previously used criteria are unclear.

OBJECTIVE

To examine associations of GD with maternal glucose metabolism and childhood adiposity 10 to 14 years' postpartum.

DESIGN, SETTING, AND PARTICIPANTS

The Hyperglycemia and Adverse Pregnancy Outcome (HAPO) Study established associations of glucose levels during pregnancy with perinatal outcomes and the follow-up study evaluated the long-term outcomes (4697 mothers and 4832 children; study visits occurred between February 13, 2013, and December 13, 2016).

EXPOSURES

Gestational diabetes was defined post hoc using criteria from the International Association of Diabetes and Pregnancy Study Groups consisting of 1 or more of the following 75-g oral glucose tolerance test results (fasting plasma glucose ≥92 mg/dL; 1-hour plasma glucose level ≥180 mg/dL; 2-hour plasma glucose level ≥153 mg/dL).

MAIN OUTCOMES AND MEASURES

Primary maternal outcome: a disorder of glucose metabolism (composite of type 2 diabetes or prediabetes). Primary outcome for children: being overweight or obese; secondary outcomes: obesity, body fat percentage, waist circumference, and sum of skinfolds (>85th percentile for latter 3 outcomes).

RESULTS

The analytic cohort included 4697 mothers (mean [SD] age, 41.7 [5.7] years) and 4832 children (mean [SD] age, 11.4 [1.2] years; 51.0% male). The median duration of follow-up was 11.4 years. The criteria for GD were met by 14.3% (672/4697) of mothers overall and by 14.1% (683/4832) of mothers of participating children. Among mothers with GD, 52.2% (346/663) developed a disorder of glucose metabolism vs 20.1% (791/3946) of mothers without GD (odds ratio [OR], 3.44 [95% CI, 2.85 to 4.14]; risk difference [RD], 25.7% [95% CI, 21.7% to 29.7%]). Among children of mothers with GD, 39.5% (269/681) were overweight or obese and 19.1% (130/681) were obese vs 28.6% (1172/4094) and 9.9% (405/4094), respectively, for children of mothers without GD. Adjusted for maternal body mass index during pregnancy, the OR was 1.21 (95% CI, 1.00 to 1.46) for children who were overweight or obese and the RD was 3.7% (95% CI, -0.16% to 7.5%); the OR was 1.58 (95% CI, 1.24 to 2.01) for children who were obese and the RD was 5.0% (95% CI, 2.0% to 8.0%); the OR was 1.35 (95% CI, 1.08 to 1.68) for body fat percentage and the RD was 4.2% (95% CI, 0.9% to 7.4%); the OR was 1.34 (95% CI, 1.08 to 1.67) for waist circumference and the RD was 4.1% (95% CI, 0.8% to 7.3%); and the OR was 1.57 (95% CI, 1.27 to 1.95) for sum of skinfolds and the RD was 6.5% (95% CI, 3.1% to 9.9%).

CONCLUSIONS AND RELEVANCE

Among women with GD identified by contemporary criteria compared with those without it, GD was significantly associated with a higher maternal risk for a disorder of glucose metabolism during long-term follow-up after pregnancy. Among children of mothers with GD vs those without it, the difference in childhood overweight or obesity defined by body mass index cutoffs was not statistically significant; however, additional measures of childhood adiposity may be relevant in interpreting the study findings.

Prediction of Gestational Diabetes Mellitus and Pre-diabetes 5 Years Postpartum using 75 g Oral Glucose Tolerance Test at 14-16 Weeks' Gestation

Early detection and treatment of women at risk for gestational diabetes mellitus (GDM) could improve perinatal and long-term outcomes in GDM women and their offspring. We explored if a 75 g oral glucose tolerance test (OGTT) at 14–16 weeks of gestation could identify women who will (1) develop GDM or give birth to large-for-gestational-age (LGA) babies in 1031 pregnant women from the STORK study using different diagnostic criteria (WHO1999, IADPSG2010, WHO2013, NORWAY2017) and (2) develop pre-diabetes 5 years postpartum focusing on first trimester β-cell function in a separate study of 300 women from the STORK cohort. The sensitivity of the 14–16 week OGTT to identify women who would develop GDM or have LGA babies was low, and we could not identify alternative cut-offs to exclude women not at risk or identify women that could benefit from early intervention. First trimester β-cell function was a stronger determinant than third trimester β-cell function of predicting maternal pre-diabetes. In conclusion, in our normal low-risk population, the 75 g OGTT at 14–16 weeks is insufficient to identify candidates for early treatment of GDM or identify women not likely to develop GDM or have LGA babies. First trimester β-cell function may predict pre-diabetes 5 years postpartum.

Combined Metabolomic Analysis of Plasma and Urine Reveals AHBA, Tryptophan and Serotonin Metabolism as Potential Risk Factors in Gestational Diabetes Mellitus (GDM)

Gestational diabetes mellitus during pregnancy has severe implications for the health of the mother and the fetus. Therefore, early prediction and an understanding of the physiology are an important part of prenatal care. Metabolite profiling is a long established method for the analysis and prediction of metabolic diseases. Here, we applied untargeted and targeted metabolomic protocols to analyze plasma and urine samples of pregnant women with and without GDM. Univariate and multivariate statistical analyses of metabolomic profiles revealed markers such as 2-hydroxybutanoic acid (AHBA), 3-hydroxybutanoic acid (BHBA), amino acids valine and alanine, the glucose-alanine-cycle, but also plant-derived compounds like sitosterin as different between control and GDM patients. PLS-DA and VIP analysis revealed tryptophan as a strong variable separating control and GDM. As tryptophan is biotransformed to serotonin we hypothesized whether serotonin metabolism might also be altered in GDM. To test this hypothesis we applied a method for the analysis of serotonin, metabolic intermediates and dopamine in urine by stable isotope dilution direct infusion electrospray ionization mass spectrometry (SID-MS). Indeed, serotonin and related metabolites differ significantly between control and GDM patients confirming the involvement of serotonin metabolism in GDM. Clustered correlation coefficient visualization of metabolite correlation networks revealed the different metabolic signatures between control and GDM patients. Eventually, the combination of selected blood plasma and urine sample metabolites improved the AUC prediction accuracy to 0.99. The detected GDM candidate biomarkers and the related systemic metabolic signatures are discussed in their pathophysiological context. Further studies with larger cohorts are necessary to underpin these observations.

Preliminary metabolomics analysis of placenta in maternal obesity

INTRODUCTION

Metabolomics identifies phenotypical groups with specific metabolic profiles, being increasingly applied to several pregnancy conditions. This is the first preliminary study analyzing placental metabolomics in normal weight (NW) and obese (OB) pregnancies.

METHODS

Twenty NW (18.5 ≤ BMI< 25 kg/m²) and eighteen OB (BMI≥ 30 kg/m²) pregnancies were studied. Placental biopsies were collected at elective caesarean section. Metabolites extraction method was optimized for hydrophilic and lipophilic phases, then analyzed with GC-MS. Univariate and PLS-DA multivariate analysis were applied.

RESULTS

Univariate analysis showed increased uracil levels while multivariate PLS-DA analysis revealed lower levels of LC-PUFA derivatives in the lipophilic phase and several metabolites with significantly different levels in the hydrophilic phase of OB vs NW.

DISCUSSION

Placental metabolome analysis of obese pregnancies showed differences in metabolites involved in antioxidant defenses, nucleotide production, as well as lipid synthesis and energy production, supporting a shift towards higher placental metabolism. OB placentas also showed a specific fatty acids profile suggesting a disruption of LC-PUFA biomagnification. This study can lay the foundation to further metabolomic placental characterization in maternal obesity. Metabolic signatures in obese placentas may reflect changes occurring in the intrauterine metabolic environment, which may affect the development of adult diseases.