Metabolomics signatures associated with an oral glucose challenge in pregnant women. Diabetes Metab. 2019;45:39-46. [PMID: 29395809 DOI: 10.1016/j.diabet.2018.01.004]

Human Milk Oligosaccharides in Maternal Serum Respond to Oral Glucose Load and Are Associated with Insulin Sensitivity

(1) Background: Pregnancy presents a challenge to maternal glucose homeostasis; suboptimal adaptations can lead to gestational diabetes mellitus (GDM). Human milk oligosaccharides (HMOs) circulate in maternal blood in pregnancy and are altered with GDM, suggesting influence of glucose homeostasis on HMOs. We thus assessed the HMO response to glucose load during an oral glucose tolerance test (OGTT) and investigated HMO associations with glucose tolerance/insulin sensitivity in healthy pregnant women. (2) Methods: Serum of 99 women, collected at 0 h, 1 h and 2 h during a 75 g OGTT at 24-28 gestational weeks was analyzed for HMOs (2'FL, 3'SLN, LDFT, 3'SL) by HPLC; plasma glucose, insulin and C-peptide were analyzed by standard biochemistry methods. (3) Results: Serum 3'SL concentrations significantly increased from fasting to 1 h after glucose load, while concentrations of the other HMOs were unaltered. Higher 3'SL at all OGTT time points was associated with a generally more diabetogenic profile, with higher hepatic insulin resistance (HOMA-IR), lower insulin sensitivity (Matsuda index) and higher insulin secretion (C-peptide index 1). (4) Conclusions: Rapid increase in serum 3'SL post-oral glucose load (fasted-fed transition) indicates utilization of plasma glucose, potentially for sialylation of lactose. Associations of sialylated HMOs with a more diabetogenic profile suggest sustained adaptations to impaired glucose homeostasis in pregnancy. Underlying mechanisms or potential consequences of observed HMO changes remain to be elucidated.

The implications of the purinergic signaling throughout pregnancy

Purinergic signaling is a necessary mechanism to trigger or even amplify cell communication. Its ligands, notably adenosine triphosphate (ATP) and adenosine, modulate specific membrane-bound receptors in virtually all human cells. Regardless of the stage of the pregnancy, cellular communication between maternal, placental, and fetal cells is the paramount mechanism to sustain its optimal status. In this review, we describe the crucial role of purinergic signaling on the regulation of the maternal-fetal trophic exchanges, immune control, and endocrine exchanges throughout pregnancy. The nature of the modulation of both ATP and adenosine on the embryo-maternal interface, going through placental invasion until birth delivery depends on the general maternal-fetal health state and consequently on the selective activation of their specific receptors. In addition, an increasing number of studies have been demonstrating the pivotal role of ATP and adenosine in modulating deleterious effects of suboptimal conditions of pregnancy. Here, we discuss the role of purinergic signaling on the balance that coordinates the embryo-maternal exchanges and a promising therapeutic venue in the context of pregnancy disorders.

Current progress in metabolomics of gestational diabetes mellitus

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

Changes in Plasma Metabolome Profiles Following Oral Glucose Challenge among Adult Chinese

Little is known about changes in plasma metabolome profiles during the oral glucose tolerance test (OGTT) in Chinese. We aimed to characterize plasma metabolomic profiles at 0 and 2 h of OGTT and their changes in individuals of different glycemic statuses. A total of 544 metabolites were detected at 0 and 2 h of OGTT by a nontarget strategy in subjects with normal glucose (n = 234), prediabetes (n = 281), and newly diagnosed type 2 diabetes (T2D) (n = 66). Regression model, mixed model, and partial least squares discrimination analysis were applied. Compared with subjects of normal glucose, T2D cases had significantly higher levels of glycerone at 0 h and 22 metabolites at 2 h of OGTT (false discovery rate (FDR) < 0.05, variable importance in projection (VIP) > 1). Seven of the twenty-two metabolites were also significantly higher in T2D than in prediabetes subjects at 2 h of OGTT (FDR < 0.05, VIP > 1). Two hours after glucose challenge, concentrations of 35 metabolites (normal: 18; prediabetes: 23; T2D: 13) significantly increased (FDR < 0.05, VIP > 1, fold change (FC) > 1.2), whereas those of 45 metabolites (normal: 36; prediabetes: 29; T2D: 18) significantly decreased (FDR < 0.05, VIP > 1, FC < 0.8). Distinct responses between cases and noncases were detected in metabolites including 4-imidazolone-5-acetate and 4-methylene-L-glutamine. More varieties of distinct metabolites across glycemic statuses were observed at 2 h of OGTT compared with fasting state. Whether the different patterns and responsiveness of certain metabolites in T2D reflect a poor resilience of specific metabolic pathways in regaining glucose homeostasis merits further study.

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.

Changes in Metabolites During an Oral Glucose Tolerance Test in Early and Mid-Pregnancy: Findings from the PEARLS Randomized, Controlled Lifestyle Trial

The oral glucose tolerance test (OGTT) is used to diagnose gestational and other types of diabetes. We examined metabolite changes during an OGTT, and how a comprehensive diet and physical activity intervention may influence these changes in a population of overweight/obese Hispanic pregnant women. Integration of changes in metabolites during an OGTT may help us gain preliminary insights into how glucose metabolism changes during pregnancy. Among women from the Pregnancy and EARly Lifestyle improvement Study (PEARLS), we measured metabolites during a multipoint OGTT (fasting, 30, 60 and 120 min) at early and mid-pregnancy. Metabolite levels were measured by liquid chromatography-mass spectrometry in plasma samples in the lifestyle intervention (n = 13) and control (n = 16) arms of the study. A total of 65 candidate metabolites were selected that displayed changes during an OGTT in previous studies. Paired and unpaired t-tests were used to examine differences in Δfast-120 min: (1) at early and mid-pregnancy; and (2) by intervention assignment. We applied principal component analysis (PCA) to identify those metabolites that differed by intervention assignment and OGTT time points. Most of the characteristic changes in metabolites post-OGTT were similar at both gestational time points. PCA identified characteristic metabolite patterns associated with OGTT time points at both early and mid-pregnancy. These metabolites included ketone bodies, tryptophan, acyl carnitines, polyunsaturated fatty acids, and biomarkers related to bile acid, urea cycle, arginine, and proline metabolism. PCA identified distinct Δfast-120 min in fatty acid, acyl carnitine, bile acid, ketone body, and amino acid levels at mid- compared to early pregnancy. Participants in the intervention group did not display mean decreases in Δfast-120 min of several long-chain acyl carnitines that were observed in the control group. These findings provide preliminary insight into metabolites, whose role in increased insulin resistance during pregnancy, should be explored further in future studies.

Improved metabolomic data-based prediction of depressive symptoms using nonlinear machine learning with feature selection

To solve major limitations in algorithms for the metabolite-based prediction of psychiatric phenotypes, a novel prediction model for depressive symptoms based on nonlinear feature selection machine learning, the Hilbert-Schmidt independence criterion least absolute shrinkage and selection operator (HSIC Lasso) algorithm, was developed and applied to a metabolomic dataset with the largest sample size to date. In total, 897 population-based subjects were recruited from the communities affected by the Great East Japan Earthquake; 306 metabolite features (37 metabolites identified by nuclear magnetic resonance measurements and 269 characterized metabolites based on the intensities from mass spectrometry) were utilized to build prediction models for depressive symptoms as evaluated by the Center for Epidemiologic Studies-Depression Scale (CES-D). The nested fivefold cross-validation was used for developing and evaluating the prediction models. The HSIC Lasso-based prediction model showed better predictive power than the other prediction models, including Lasso, support vector machine, partial least squares, random forest, and neural network. L-leucine, 3-hydroxyisobutyrate, and gamma-linolenyl carnitine frequently contributed to the prediction. We have demonstrated that the HSIC Lasso-based prediction model integrating nonlinear feature selection showed improved predictive power for depressive symptoms based on metabolome data as well as on risk metabolites based on nonlinear statistics in the Japanese population. Further studies should use HSIC Lasso-based prediction models with different ethnicities to investigate the generality of each risk metabolite for predicting depressive symptoms.

Metabolomic markers of antepartum depression and suicidal ideation

BACKGROUND

Recent analyses have described metabolomic markers for depression and suicidal ideation in non-pregnant adults. We examined the metabolomic profile of antepartum depression and suicidal ideation during mid-pregnancy, a time of high susceptibility to mood disorders.

METHODS

We collected fasting blood from 100 pregnant Peruvian women and profiled 307 plasma metabolites using liquid chromatography-mass spectrometry. We used the Patient Health Questionnaire 9 to define antepartum depression (score  ≥ 10) and suicidal ideation (having thoughts that you would be better off dead, or of hurting yourself). Logistic regression was used to calculate odds ratios (ORs).

RESULTS

Three triacylglycerol metabolites (C48:5 triacylglycerol [OR = =1.89; 95% confidence interval (CI): 1.14-3.14], C50:6 triacylglycerol [OR = =1.88; 95%CI: 1.13-3.14], C46:4 triacylglycerol [OR = =1.89; 95%CI: 1.11-3.21]) were associated with higher odds of antepartum depression and 4 metabolites (betaine [OR = =0.56; 95%CI:0.33-0.95], citrulline [OR = =0.58; 95%CI: 0.34-0.98], C5 carnitine [OR = =0.59; 95%CI: 0.36-0.99], C5:1 carnitine [OR = =0.59; 95%CI: 0.35-1.00]) with lower odds of antepartum depression. Twenty-six metabolites, including 5-hydroxytryptophan (OR = =0.52; 95%CI: 0.30-0.92), phenylalanine (OR = =0.41; 95%CI: 0.19-0.91), and betaine (OR = =0.53; 95%CI: 0.28-0.99) were associated with lower odds of suicidal ideation.

LIMITATIONS

Our cross-sectional study could not determine whether metabolites prospectively predict outcomes. No metabolites remained significant after multiple testing correction; these novel findings should be replicated in a larger sample.

CONCLUSIONS

Antepartum suicidal ideation metabolomic markers are similar to markers of depression among non-pregnant adults, and distinct from markers of antepartum depression. Findings suggest that mood disorder in pregnancy shares metabolomic similarities to mood disorder at other times and may further understanding of these conditions' pathophysiology.

Beneficial Effects of n-3 Polyunsaturated Fatty Acids on Offspring's Pancreas of Gestational Diabetes Rats

We studied the long-term influence of gestational diabetes mellitus (GDM) on the pancreas of offspring and the effect of omega-3 polyunsaturated fatty acids (n-3 PUFAs) on offspring's pancreas. GDM offspring were divided into three groups: GDM offspring, n-3 PUFA-adequate-GDM offspring, and n-3 PUFA-deficient GDM offspring. All healthy and GDM offspring were fed up to 11 months old. The pancreas of GDM offspring exhibited fatty infiltration at 11 months old, whereas n-3 PUFA improved the pancreatic fatty infiltration. n-3 PUFA lowered the pancreatic oxidative stress and inflammation. Surprisingly, n-3 PUFA postponed pancreatic telomere shortening of GDM offspring at old age. Nontargeted metabolomics showed that many metabolites were altered in the pancreas of GDM offspring at old age, including l-valine, ceramide, acylcarnitines, tocotrienol, cholesteryl acetate, and biotin. n-3 PUFA modulated some altered metabolites and metabolic pathways. Therefore, GDM caused the long-term effects on offspring's pancreas, whereas n-3 PUFA played a beneficial role.

N-3 Polyunsaturated Fatty Acids Decrease Long-Term Diabetic Risk of Offspring of Gestational Diabetes Rats by Postponing Shortening of Hepatic Telomeres and Modulating Liver Metabolism

The long-term influence of gestational diabetes mellitus (GDM) on offspring and the effect of omega-3 polyunsaturated fatty acids (n-3 PUFA) on GDM offspring are poorly understood. We studied the long-term diabetic risk in GDM offspring and evaluated the effect of n-3 PUFA intervention. Healthy offspring rats were fed standard diet (soybean oil) after weaning. GDM offspring were divided into three groups: GDM offspring (soybean oil), n-3 PUFA adequate offspring (fish oil), and n-3 PUFA deficient offspring (safflower oil), fed up to 11 months old. The diabetic risk of GDM offspring gradually increased from no change at weaning to obvious impaired glucose and insulin tolerance at 11 months old. n-3 PUFA decreased oxidative stress and inflammation in the liver of older GDM offspring. There was a differential effect of n-3 PUFA and n-6 PUFA on hepatic telomere length in GDM offspring. Non-targeted metabolomics showed that n-3 PUFA played a modulating role in the liver, in which numerous metabolites and metabolic pathways were altered when GDM offspring grew to old age. Many metabolites were related to diabetes risk, such as α-linolenic acid, palmitic acid, ceramide, oxaloacetic acid, tocotrienol, tetrahydro-11-deoxycortisol, andniacinamide. In summary, GDM offspring exhibited obvious diabetes risk at old age, whereas n-3 PUFA decreased this risk.

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.

Metabolic Signature of Dietary Iron Overload in a Mouse Model

Iron is an essential co-factor for several metabolic processes, including the Krebs cycle and mitochondrial oxidative phosphorylation. Therefore, maintaining an appropriate iron balance is essential to ensure sufficient energy production and to avoid excessive reactive oxygen species formation. Iron overload impairs mitochondrial fitness; however, little is known about the associated metabolic changes. Here we aimed to characterize the metabolic signature triggered by dietary iron overload over time in a mouse model, where mice received either a standard or a high-iron diet. Metabolic profiling was assessed in blood, plasma and liver tissue. Peripheral blood was collected by means of volumetric absorptive microsampling (VAMS). Extracted blood and tissue metabolites were analyzed by liquid chromatography combined to high resolution mass spectrometry. Upon dietary iron loading we found increased glucose, aspartic acid and 2-/3-hydroxybutyric acid levels but low lactate and malate levels in peripheral blood and plasma, pointing to a re-programming of glucose homeostasis and the Krebs cycle. Further, iron loading resulted in the stimulation of the urea cycle in the liver. In addition, oxidative stress was enhanced in circulation and coincided with increased liver glutathione and systemic cysteine synthesis. Overall, iron supplementation affected several central metabolic circuits over time. Hence, in vivo investigation of metabolic signatures represents a novel and useful tool for getting deeper insights into iron-dependent regulatory circuits and for monitoring of patients with primary and secondary iron overload, and those ones receiving iron supplementation therapy.

The Emerging Role of Biomarkers in the Diagnosis of Gestational Diabetes Mellitus

Gestational diabetes mellitus (GDM) is a common complication of pregnancy; its rising incidence is a result of increased maternal obesity and older maternal age together with altered diagnostic criteria identifying a greater proportion of pregnant women with GDM. Its consequences are far-reaching, associated with poorer maternal and neonatal outcomes compared to non-GDM pregnancies, and GDM has implications for metabolic health in both mother and offspring. Objective markers to identify women at high risk for the development of GDM are useful to target therapy and potentially prevent its development. Established clinical risk factors for GDM include overweight/obesity, age, ethnicity, and family history of diabetes, though they lack specificity for its development. The addition of biomarkers to predictive models of GDM may improve the ability to identify women at risk of GDM prior to its development. These biomarkers reflect the pathophysiologic mechanisms of GDM involving insulin resistance, chronic inflammation, and altered placental function. In addition, the role of epigenetic changes in GDM pathogenesis highlights the complex interplay between genetic and environmental factors, potentially offering further refinement of the prediction of GDM risk. In this review, we will discuss the clinical challenges associated with the diagnosis of GDM and its current pathophysiologic basis, giving rise to potential biomarkers that may aid in its identification. While not yet validated for clinical use, we explore the possible clinical role of biomarkers in the future. We also explore novel diagnostic tools, including high throughput methodologies, that may have potential future application in the identification of women with GDM.