Amino acids levels in early pregnancy predict subsequent gestational diabetes

Association of maternal blood metabolomics and gestational diabetes mellitus risk: a systematic review and meta-analysis

Gestational diabetes mellitus (GDM) is a common complication of pregnancy that has short- and long-term adverse effects. Therefore, further exploration of the pathophysiology of GDM and related biomarkers is important. In this study, we performed a systematic review and meta-analysis to investigate the associations between metabolites in blood detected via metabolomics techniques and the risk of GDM and to identify possible biomarkers for predicting the occurrence of GDM. We retrieved case‒control and cohort studies of metabolomics and GDM published in PubMed, Embase, and Web of Science through March 29, 2024; extracted metabolite concentrations, odds ratios (ORs), or relative risks (RRs); and evaluated the integrated results with metabolites per-SD risk estimates and 95% CIs for GDM. We estimated the results via the random effects model and the inverse variance method. Our study is registered in PROSPERO (CRD42024539435). We included a total of 28 case‒control and cohort studies, including 17,370 subjects (4,372 GDM patients and 12,998 non-GDM subjects), and meta-analyzed 67 metabolites. Twenty-five of these metabolites were associated with GDM risk. Some amino acids (isoleucine, leucine, valine, alanine, aspartate, etc.), lipids (C16:0, C18:1n-9, C18:1n-7, lysophosphatidylcholine (LPC) (16:0), LPC (18:0), and palmitoylcarnitine), and carbohydrates and energy metabolites (glucose, pyruvate, lactate, 2-hydroxybutyrate, 3-hydroxybutyrate) were discovered to be associated with increased GDM risk (hazard ratio 1.06-2.77). Glutamine, histidine, C14:0, and sphingomyelin (SM) (34:1) were associated with lower GDM risk (hazard ratio 0.75-0.84). These findings suggest that these metabolites may play essential roles in GDM progression, and serve as biomarkers, contributing to the early diagnosis and prediction of GDM.

Titanium exposure and gestational diabetes mellitus: associations and potential mediation by perturbation of amino acids in early pregnancy

BACKGROUND

Several recent studies reported the potential adverse effects of titanium exposure on glucose homeostasis among the non-pregnant population, but the association of titanium exposure with gestational diabetes mellitus (GDM) is scarce.

METHODS

The present study of 1,449 pregnant women was conducted within the Jiangsu Birth Cohort (JBC) study in China. Urine samples were collected in the early pregnancy, and urinary titanium concentration and non-targeted metabolomics were measured. Poisson regression estimated the association of titanium exposure in the early pregnancy with subsequent risk of GDM. Multiple linear regression screened for titanium-related urine metabolites. Mediation analyses assessed the mediating effects of candidate metabolites and pathways.

RESULTS

As parameterized in tertiles, titanium showed positive dose-response relationship with GDM risk (P for trend = 0.008), with women at the highest tertile of titanium exposure having 30% increased risk of GDM [relative risk (RR) = 1.30 (95% CI: 1.06, 1.61)] when compared to those exposure at the first tertile level. Meanwhile, we identified the titanium-related metabolites involved in four amino acid metabolic pathways. Notably, the perturbation of the aminoacyl-tRNA biosynthesis and alanine, aspartate and glutamate metabolism mediated 27.1% and 31.0%, respectively, of the relative effect of titanium exposure on GDM. Specifically, three titanium-related metabolites, choline, creatine and L-alanine, demonstrated predominant mediation effects on the association between titanium exposure and GDM risk.

CONCLUSIONS

In this prospective study, we uniquely identified a correlation between early pregnancy titanium exposure and increased GDM risk. We unveiled novel insights into how perturbations in amino acid metabolism may mediate the link between titanium exposure and GDM. Notably, choline, creatine, and L-alanine emerged as key mediators influencing this association. Our findings imply that elevated titanium exposure in early pregnancy can lead to amino acid dysmetabolism, thereby elevating GDM risk.

The association between circulating phenylalanine and the temporal risk of impaired insulin markers in gestational diabetes mellitus

Background

We aimed to contrast plasma amino acid concentrations in pregnant women with Gestational Diabetes Mellitus (GDM) to those without, to analyze the link between plasma amino acid concentrations, GDM, insulin resistance, and insulin secretion at 24-28 weeks of gestation.

Methods

The research employed a retrospective case-control study design at a single center. Basic demographic and laboratory data were procured from the hospital's case system. The study encompassed seventy women without gestational diabetes mellitus (GDM) and thirty-five women with GDM matched in a 1-to-2 ratio for age and pre-pregnancy BMI. Utilizing high-performance liquid chromatography-mass spectrometry (HPLC-MS), peripheral fasting plasma amino acid concentrations in these women, during mid-pregnancy, were duly measured. We carefully evaluated the significant differences in the quantitative data between the two groups and developed linear regression models to assess the independent risk factors affecting insulin resistance and insulin secretion.

Results

Significant variations in insulin secretion and resistance levels distinguished GDM Group from the non-GDM group at three distinct time points, alongside relatively elevated serum Glycosylated Hemoglobin (HbA1c) levels. Triglycerides (TG) were also significantly increased in those with GDM during adipocytokine observations. Apart from glutamic acid and glutamine, the concentrations of the remaining 16 amino acids were notably increased in GDM patients, including all branched chain amino acids(BCAAs) and aromatic amino acids(AAAs). Ultimately, it was ascertained that fasting serum phenylalanine levels were independent risk factors affecting insulin resistance index and insulin secretion at various phases.

Conclusions

Various fasting serum amino acid levels are markedly increased in patients with GDM, specifically phenylalanine, which may play role in insulin resistance and secretion.

Impairments of insulin and glucagon sensitivity in Chinese women with gestational diabetes mellitus

AIM

To evaluate insulin and glucagon sensitivity in Han Chinese women with and without gestational diabetes mellitus (GDM).

METHODS

In total, 81 women with GDM and 81 age-matched healthy controls were evaluated with a 75 g oral glucose tolerance test (OGTT) at gestational weeks 24-28. Plasma glucose concentrations were measured at fasting and 1 h and 2 h post-OGTT. Fasting plasma insulin, glucagon and amino acids were also measured. Insulin and glucagon sensitivity were assessed by the homeostatic model assessment of insulin resistance (HOMA-IR) and glucagon-alanine index, respectively.

RESULTS

As expected, plasma glucose concentrations were higher at fasting and 1 h and 2 h post-OGTT in GDM participants (p < .001 each). Both the HOMA-IR and the glucagon-alanine index were higher in GDM participants. There was a weak positive correlation between HOMA-IR and glucagon-alanine index (r = 0.24, p = .0024). Combining the HOMA-IR and the glucagon-alanine index yielded better capacity (area under the curve = 0.878) than either alone (area under the curve = 0.828 for HOMA-IR and 0.751 for glucagon-alanine index, respectively) in differentiating GDM from healthy participants. While the majority of GDM participants (64%) exhibited both reduced insulin and glucagon sensitivity, a third of them presented either reduced insulin (20%) or glucagon (14%) sensitivity alone. HOMA-IR and glucagon-alanine index correlated differentially with fasting glucose, triglycerides, low-density lipoprotein cholesterol, sum of amino acids and hepatic steatosis index.

CONCLUSIONS

Impairments of both insulin and glucagon sensitivity occur frequently in Chinese women with GDM, which may, individually or together, drive metabolic derangements in GDM. These observations provide new insights into the pathophysiology of GDM and support the need to target insulin or glucagon resistance, or both, in the management of GDM.

Relationship between gut microbiota and the pathogenesis of gestational diabetes mellitus: a systematic review

Introduction

Gestational diabetes mellitus (GDM) is a form of gestational diabetes mellitus characterized by insulin resistance and abnormal function of pancreatic beta cells. In recent years, genomic association studies have revealed risk and susceptibility genes associated with genetic susceptibility to GDM. However, genetic predisposition cannot explain the rising global incidence of GDM, which may be related to the increased influence of environmental factors, especially the gut microbiome. Studies have shown that gut microbiota is closely related to the occurrence and development of GDM. This paper reviews the relationship between gut microbiota and the pathological mechanism of GDM, in order to better understand the role of gut microbiota in GDM, and to provide a theoretical basis for clinical application of gut microbiota in the treatment of related diseases.

Methods

The current research results on the interaction between GDM and gut microbiota were collected and analyzed through literature review. Keywords such as "GDM", "gut microbiota" and "insulin resistance" were used for literature search, and the methodology, findings and potential impact on the pathophysiology of GDM were systematically evaluated.

Results

It was found that the composition and diversity of gut microbiota were significantly associated with the occurrence and development of GDM. Specifically, the abundance of certain gut bacteria is associated with an increased risk of GDM, while other changes in the microbiome may be associated with improved insulin sensitivity. In addition, alterations in the gut microbiota may affect blood glucose control through a variety of mechanisms, including the production of short-chain fatty acids, activation of inflammatory pathways, and metabolism of the B vitamin group.

Discussion

The results of this paper highlight the importance of gut microbiota in the pathogenesis of GDM. The regulation of the gut microbiota may provide new directions for the treatment of GDM, including improving insulin sensitivity and blood sugar control through the use of probiotics and prebiotics. However, more research is needed to confirm the generality and exact mechanisms of these findings and to explore potential clinical applications of the gut microbiota in the management of gestational diabetes. In addition, future studies should consider the interaction between environmental and genetic factors and how together they affect the risk of GDM.

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.

Longitudinal metabolomics integrated with machine learning identifies novel biomarkers of gestational diabetes mellitus

BACKGROUND

Evidence from longitudinal studies is crucial to enhance our understanding of the role of metabolites in the progression of gestational diabetes mellitus (GDM). Herein, a longitudinal untargeted metabolomic study was conducted to reveal the metabolomic profiles and biomarkers associated with the progression of GDM, and characterize the changing patterns of metabolites.

METHODS

We collected serum samples at three trimesters from 30 patients with GDM and 30 healthy Chinese pregnant women with pre-pregnancy BMI, age, and parity matched, and untargeted metabolomic analysis was performed, followed by machine learning approaches that integrated bootstrap and LASSO. Cluster analysis was conducted to elucidate the patterns of metabolite changes. Pathway analyses were conducted to gain insights into the underlying pathways involved.

RESULTS

A total of 32 metabolites, mainly belonging to amino acid and its derivatives, were significantly associated with GDM across three trimesters, and were clustered into three distinct patterns. Metabolites belonging to phosphatidylcholines, lysophosphatidylcholines, lysophosphatidic acids, and lysophosphatidylethanolamines were consistently upregulated, and 2,3-Dihydroxypropyl dihydrogen phosphate was downregulated in GDM group. Amino acid-related, glycerophospholipid, and vitamin B6 metabolism were enriched in multiple trimesters. The levels of allantoic acid, which was positively correlated with blood glucose, was consistently higher in GDM patients and exhibited good discriminatory ability for GDM in the early and mid-pregnancy.

CONCLUSION

We identified and characterized distinct patterns of metabolites associated with GDM throughout pregnancy, and found that allantoic acid was a potential biomarker for early diagnosis of GDM.

Biomarkers of Postpartum Depression: A Narrative Review

Postpartum depression (PPD) is a disorder that impairs the formation of the relationship between mother and child, and reduces the quality of life for affected women to a functionally significant degree. Studying markers associated with PPD can help in early detection, prevention, or monitoring treatment. The purpose of this paper is to review biomarkers linked to PPD and to present selected theories on the pathogenesis of the disease based on data from biomarker studies. The complex etiology of the disorder reduces the specificity and sensitivity of markers, but they remain a valuable source of information to help clinicians. The biggest challenge of the future will be to translate high-tech methods for detecting markers associated with postpartum depression into more readily available and less costly ones. Population-based studies are needed to test the utility of potential PPD markers.

Proteins and metabolites fingerprints of gestational diabetes mellitus forming protein-metabolite interactomes are its potential biomarkers

Gestational diabetes mellitus (GDM) is a consequence of glucose intolerance with an inadequate production of insulin that happens during pregnancy and leads to adverse health consequences for both mother and fetus. GDM patients are at higher risk for preeclampsia, and developing diabetes mellitus type 2 in later life, while the child born to GDM mothers are more prone to macrosomia, and hypoglycemia. The universally accepted diagnostic criteria for GDM are lacking, therefore there is a need for a diagnosis of GDM that can identify GDM at its early stage (first trimester). We have reviewed the literature on proteins and metabolites fingerprints of GDM. Further, we have performed protein-protein, metabolite-metabolite, and protein-metabolite interaction network studies on GDM proteins and metabolites fingerprints. Notably, some proteins and metabolites fingerprints are forming strong interaction networks at high confidence scores. Therefore, we have suggested that those proteins and metabolites that are forming protein-metabolite interactomes are the potential biomarkers of GDM. The protein-metabolite biomarkers interactome may help in a deep understanding of the prognosis, pathogenesis of GDM, and also detection of GDM. The protein-metabolites interactome may be further applied in planning future therapeutic strategies to promote long-term health benefits in GDM mothers and their children.

Aromatic Amino Acids and Their Interactions with Gut Microbiota-Related Metabolites for Risk of Gestational Diabetes: A Prospective Nested Case-Control Study in a Chinese Cohort

INTRODUCTION

The aim of this study was to explore associations of aromatic amino acids (AAA) in early pregnancy with gestational diabetes mellitus (GDM), and whether high AAA and gut microbiota-related metabolites had interactive effects on GDM risk.

METHODS

We conducted a 1:1 case-control study (n = 486) nested in a prospective cohort of pregnant women from 2010 to 2012. According to the International Association of Diabetes and Pregnancy Study Group's criteria, 243 women were diagnosed with GDM. Binary conditional logistic regression was performed to examine associations of AAA with GDM risk. Interactions between AAA and gut microbiota-related metabolites for GDM were examined using additive interaction measures.

RESULTS

High phenylalanine and tryptophan were associated with increased GDM risk (OR: 1.72, 95% CI: 1.07-2.78 and 1.66, 1.02-2.71). The presence of high trimethylamine (TMA) markedly increased the OR of high phenylalanine alone up to 7.95 (2.79-22.71), while the presence of low glycoursodeoxycholic acid (GUDCA) markedly increased the OR of high tryptophan alone up to 22.88 (5.28-99.26), both with significant additive interactions. Furthermore, high lysophosphatidylcholines (LPC18:0) mediated both interactive effects.

CONCLUSIONS

High phenylalanine may have an additive interaction with high TMA, while high tryptophan may have an additive interaction with low GUDCA toward increased risk of GDM, both being mediated via LPC18:0.

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.

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.

Gestational diabetes is driven by microbiota-induced inflammation months before diagnosis

OBJECTIVE

Gestational diabetes mellitus (GDM) is a condition in which women without diabetes are diagnosed with glucose intolerance during pregnancy, typically in the second or third trimester. Early diagnosis, along with a better understanding of its pathophysiology during the first trimester of pregnancy, may be effective in reducing incidence and associated short-term and long-term morbidities.

DESIGN

We comprehensively profiled the gut microbiome, metabolome, inflammatory cytokines, nutrition and clinical records of 394 women during the first trimester of pregnancy, before GDM diagnosis. We then built a model that can predict GDM onset weeks before it is typically diagnosed. Further, we demonstrated the role of the microbiome in disease using faecal microbiota transplant (FMT) of first trimester samples from pregnant women across three unique cohorts.

RESULTS

We found elevated levels of proinflammatory cytokines in women who later developed GDM, decreased faecal short-chain fatty acids and altered microbiome. We next confirmed that differences in GDM-associated microbial composition during the first trimester drove inflammation and insulin resistance more than 10 weeks prior to GDM diagnosis using FMT experiments. Following these observations, we used a machine learning approach to predict GDM based on first trimester clinical, microbial and inflammatory markers with high accuracy.

CONCLUSION

GDM onset can be identified in the first trimester of pregnancy, earlier than currently accepted. Furthermore, the gut microbiome appears to play a role in inflammation-induced GDM pathogenesis, with interleukin-6 as a potential contributor to pathogenesis. Potential GDM markers, including microbiota, can serve as targets for early diagnostics and therapeutic intervention leading to prevention.

Omics biomarkers and an approach for their practical implementation to delineate health status for personalized nutrition strategies

Personalized nutrition (PN) has gained much attention as a tool for empowerment of consumers to promote changes in dietary behavior, optimizing health status and preventing diet related diseases. Generalized implementation of PN faces different obstacles, one of the most relevant being metabolic characterization of the individual. Although omics technologies allow for assessment the dynamics of metabolism with unprecedented detail, its translatability as affordable and simple PN protocols is still difficult due to the complexity of metabolic regulation and to different technical and economical constrains. In this work, we propose a conceptual framework that considers the dysregulation of a few overarching processes, namely Carbohydrate metabolism, lipid metabolism, inflammation, oxidative stress and microbiota-derived metabolites, as the basis of the onset of several non-communicable diseases. These processes can be assessed and characterized by specific sets of proteomic, metabolomic and genetic markers that minimize operational constrains and maximize the information obtained at the individual level. Current machine learning and data analysis methodologies allow the development of algorithms to integrate omics and genetic markers. Reduction of dimensionality of variables facilitates the implementation of omics and genetic information in digital tools. This framework is exemplified by presenting the EU-Funded project PREVENTOMICS as a use case.

Plasma Amino Acids in Early Pregnancy and Midpregnancy and Their Interplay With Phospholipid Fatty Acids in Association With the Risk of Gestational Diabetes Mellitus: Results From a Longitudinal Prospective Cohort

OBJECTIVE

We prospectively evaluated plasma amino acids (AAs) in early pregnancy and midpregnancy and their interplay with phospholipid fatty acids (FAs) in association with gestational diabetes mellitus (GDM) risk.

RESEARCH DESIGN AND METHODS

From a longitudinal pregnancy cohort of 2,802 individuals, concentrations of 24 plasma AAs at 10-14 and 15-26 gestational weeks (GW) were assessed among 107 GDM case subjects and 214 non-GDM control subjects. We estimated adjusted odds ratios (OR) and 95% CI for the associations of plasma AAs and the joint associations of plasma AAs and phospholipid FAs with GDM risk, adjusting for risk factors including age, prepregnancy BMI, and family history of diabetes.

RESULTS

Glycine at 10-14 GW was inversely associated with GDM (adjusted OR [95% CI] per SD increment: 0.55 [0.39-0.79]). Alanine, aspartic acid, and glutamic acid at 10-14 GW were positively associated with GDM (1.43 [1.08-1.88], 1.41 [1.11-1.80], and 1.39 [0.98-1.98]). At 15-26 GW, findings for glycine, alanine, aspartic acid, and the glutamine-to-glutamic acid ratio were consistent with the directions observed at 10-14 GW. Isoleucine, phenylalanine, and tyrosine were positively associated with GDM (1.64 [1.19-2.27], 1.15 [0.87-1.53], and 1.56 [1.16-2.09]). All P values for linear trend were <0.05. Several AAs and phospholipid FAs were significantly and jointly associated with GDM. For instance, the lowest risk was observed among women with higher glycine and lower even-chain saturated FAs at 10-14 GW (adjusted OR [95% CI] 0.15 [0.06, 0.37]).

CONCLUSIONS

Plasma AAs may be implicated in GDM development starting in early pregnancy. Associations of AAs with GDM may be enhanced in the copresence of phospholipid FA profile.

Metabolomics in gestational diabetes mellitus: A review

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

Relationship between Components, Intestinal Microbiota, and Mechanism of Hypoglycemic Effect of the Saggy Ink Cap Medicinal Mushroom (Coprinus Comatus, Agaricomycetes): A Review

Coprinus comatus is rich in a variety of nutrients, which has been reported to display a good hypoglycemic effect. However, there is no consensus on the hypoglycemic mechanism of this mushroom. Intestinal microbiota, a complex and intrinsic system, is closely related to metabolism. In this review, we discussed the potential relationship between certain components of C. comatus and intestinal microbiota to illustrate the possible hypoglycemic mechanism of C. comatus through intestinal microbiota. It will provide a new perspective for the study of hypoglycemic mechanism of C. comatus and promote the development and utilization of this mushroom.

The Role of the Kynurenine Pathway in the (Patho) physiology of Maternal Pregnancy and Fetal Outcomes: A Systematic Review

INTRODUCTION

Tryptophan is the precursor of kynurenine pathway (KP) metabolites which regulate immune tolerance, energy metabolism, and vascular tone. Since these processes are important during pregnancy, changes in KP metabolite concentrations may play a role in the pathophysiology of pregnancy complications. We hypothesize that KP metabolites can serve as novel biomarkers and preventive therapeutic targets. This review aimed to provide more insight into associations between KP metabolite concentrations in maternal and fetal blood, and in the placenta, and adverse maternal pregnancy and fetal outcomes.

METHODS

A systematic search was performed on 18 February 2022 comprising all KP metabolites, and keywords related to maternal pregnancy and fetal outcomes. English-written human studies measuring KP metabolite(s) in maternal or fetal blood or in the placenta in relation to pregnancy complications, were included. Methodological quality was assessed using the ErasmusAGE quality score (QS) (range: 0-10). A meta-analysis of the mean maternal tryptophan and kynurenine concentrations in uncomplicated pregnancies was conducted.

RESULTS

Of the 6262 unique records, 37 were included (median QS = 5). Tryptophan was investigated in most studies, followed by kynurenine, predominantly in maternal blood (n = 28/37), and in the second and third trimester of pregnancy (n = 29/37). Compared to uncomplicated pregnancies, decreased tryptophan in maternal blood was associated with an increased prevalence of depression, gestational diabetes mellitus, fetal growth restriction, spontaneous abortion, and preterm birth. Elevated tryptophan was only observed in women with pregnancy-induced hypertension compared to normotensive pregnant women. In women with preeclampsia, only kynurenic acid was altered; elevated in the first trimester of pregnancy, and positively associated with proteinuria in the third trimester of pregnancy.

CONCLUSIONS

KP metabolite concentrations were altered in a variety of maternal pregnancy and fetal complications. This review implies that physiological pregnancy requires a tight balance of KP metabolites, and that disturbances in either direction are associated with adverse maternal pregnancy and fetal outcomes.

Gut Microbiota Associated with Gestational Health Conditions in a Sample of Mexican Women

Gestational diabetes (GD), pre-gestational diabetes (PD), and pre-eclampsia (PE) are morbidities affecting gestational health which have been associated with dysbiosis of the mother's gut microbiota. This study aimed to assess the extent of change in the gut microbiota diversity, short-chain fatty acids (SCFA) production, and fecal metabolites profile in a sample of Mexican women affected by these disorders. Fecal samples were collected from women with GD, PD, or PE in the third trimester of pregnancy, along with clinical and biochemical data. Gut microbiota was characterized by high-throughput DNA sequencing of V3-16S rRNA gene libraries; SCFA and metabolites were measured by High-Pressure Liquid Chromatography (HPLC) and (Fourier Transform Ion Cyclotron Mass Spectrometry (FT-ICR MS), respectively, in extracts prepared from feces. Although the results for fecal microbiota did not show statistically significant differences in alfa diversity for GD, PD, and PE concerning controls, there was a difference in beta diversity for GD versus CO, and a high abundance of Proteobacteria, followed by Firmicutes and Bacteroidota among gestational health conditions. DESeq2 analysis revealed bacterial genera associated with each health condition; the Spearman's correlation analyses showed selected anthropometric, biochemical, dietary, and SCFA metadata associated with specific bacterial abundances, and although the HPLC did not show relevant differences in SCFA content among the studied groups, FT-ICR MS disclosed the presence of interesting metabolites of complex phenolic, valeric, arachidic, and caprylic acid nature. The major conclusion of our work is that GD, PD, and PE are associated with fecal bacterial microbiota profiles, with distinct predictive metagenomes.

Prepregnancy Protein Source and BCAA Intake Are Associated with Gestational Diabetes Mellitus in the CARDIA Study

Diet quality and protein source are associated with type 2 diabetes, however relationships with GDM are less clear. This study aimed to determine whether prepregnancy diet quality and protein source are associated with gestational diabetes mellitus (GDM). Participants were 1314 Black and White women without diabetes, who had at least one birth during 25 years of follow-up in the Coronary Artery Risk Development in Young Adults (CARDIA) cohort study. The CARDIA A Priori Diet Quality Score (APDQS) was assessed in the overall cohort at enrollment and again at Year 7. Protein source and branched-chain amino acid (BCAA) intake were assessed only at the Year 7 exam (n = 565). Logistic regression analysis was used to determine associations between prepregnancy dietary factors and GDM. Women who developed GDM (n = 161) were more likely to have prepregnancy obesity and a family history of diabetes (p < 0.05). GDM was not associated with prepregnancy diet quality at enrollment (Year 0) (odds ratio [OR]: 1.01; 95% confidence interval [CI] 0.99, 1.02) or Year 7 (odds ratio [OR]: 0.97; 95% confidence interval [CI] 0.94, 1.00) in an adjusted model. Conversely, BCAA intake (OR:1.59, 95% CI 1.03, 2.43) and animal protein intake (OR: 1.06, 95% CI 1.02, 1.10) as a proportion of total protein intake, were associated with increased odds of GDM, while proportion of plant protein was associated with decreased odds of GDM (OR: 0.95, 95% CI 0.91, 0.99). In conclusion, GDM is strongly associated with source of prepregnancy dietary protein intake but not APDQS in the CARDIA study.

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.

Branched-Chain Amino Acids and Their Interactions With Lipid Metabolites for Increased Risk of Gestational Diabetes

OBJECTIVE

We aimed to explore associations of branched-chain amino acids (BCAA) in early pregnancy with gestational diabetes mellitus (GDM), and whether high BCAAs and lipidomics markers had interactive effects on the risk of GDM.

METHODS

We conducted a 1:1 case-control study (n = 486) nested in a prospective cohort of pregnant women in Tianjin, China. Blood samples were collected at their first antenatal care visit (median 10 gestational weeks). Serum BCAAs, saturated fatty acids (SFA) and lysophosphatidylcholines (LPC) were measured by liquid chromatography-tandem mass spectrometry analysis. Conditional logistic regression was performed to examine associations of BCAAs with the risk of GDM. Interactions between high BCAAs and high SFA16:0 for GDM were examined using additive interaction measures.

RESULTS

High serum valine, leucine, isoleucine, and total BCAAs were associated with markedly increased risk of GDM (OR of top vs bottom tertiles: 1.91 [95% CI, 1.22-3.01]; 1.87 [1.20-2.91]; 2.23 [1.41-3.52]; 1.93 [1.23-3.02], respectively). The presence of high SFA16:0 defined as ≥ 17.1 nmol/mL (ie, median) markedly increased the ORs of high leucine alone and high isoleucine alone up to 4.56 (2.37-8.75) and 4.41 (2.30-8.43) for the risk of GDM, with significant additive interaction. After adjustment for LPCs, the ORs were greatly elevated (6.33, 2.25-17.80 and 6.53, 2.39-17.86) and the additive interactions became more significant.

CONCLUSION

BCAAs in early pregnancy were positively associated with the risk of GDM, and high levels of leucine and isoleucine enhanced the risk association of high SFA16:0 with GDM, independent of LPCs.

Umbilical cord blood metabolomics: association with intrauterine hyperglycemia

BACKGROUND

Intrauterine hyperglycemia can harm a fetus's growth and development, and this can be seen in the umbilical cord blood metabolism disorder. However, the metabolites and metabolic mechanisms involved in the condition remain unknown.

METHODS

Targeted metabolomics using liquid chromatography and MetaboAnalyst were conducted in this study to explore differences in metabolites and metabolic pathways between individuals with hyperglycemia or well-controlled gestational diabetes mellitus (GDM) and healthy controls.

RESULTS

Univariate analysis found that the hyperglycemic and healthy control groups differed in 30 metabolites, while the well-controlled GDM and the healthy control groups differed only in three metabolites-ursodeoxycholic acid, docosahexaenoic acid, and 8,11,14-eicosatrienoic acid. Most of these metabolic variations were negatively associated with neonatal weights. Further research showed that the variations in the metabolites were primarily associated with the metabolic pathways of linoleic acid (LA) and alpha-linolenic acid (ALA).

CONCLUSION

Gestational hyperglycemia and well-controlled GDM, which may play a major role by inhibiting the LA and ALA metabolic pathways, have detrimental effects on cord blood metabolism.

IMPACT

The main point of this paper is that intrauterine hyperglycemia has a negative effect on cord blood metabolism mainly through the linoleic acid and alpha-linolenic acid metabolic pathways. This is a study to report a new association between well-controlled GDM and cord blood metabolism. This study provides a possible explanation for the association between intrauterine hyperglycemia and neonatal adverse birth outcomes.

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

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

Changes in the Gut Metabolic Profile of Gestational Diabetes Mellitus Rats Following Probiotic Supplementation

The roles of gut microbiota and metabolomics in women with gestational diabetes mellitus (GDM) are not well understood. This study investigated the gut metabolomic profiling of GDM rats and GDM rats treated with probiotic supplements. Associations between gut metabolites and microbiota were also studied in GDM rats. Liquid chromatography–mass spectrometry was used to detect gut metabolites in GDM rats and GDM rats treated with probiotic supplements of 0.5 g (low-dose group) or 1 g (high-dose group) for 15 days. Each gram of probiotic supplement contained 5 × 107 colony-forming units (CFU) of Lactobacillus rhamnosus LGG and 1 × 108 CFU of Bifidobacterium animalis subspecies lactis Bb12. The association between gut metabolites and microbiota in GDM rats was investigated using Spearman’s correlation. Finally, 10 rats in the normal pregnant group, eight rats in the GDM model group, eight GDM rats in the low-dose probiotics group, and nine GDM rats in the high-dose probiotics group were further studied. Serum parameters and pancreatic and colon histology were significantly changed in GDM rats, and these were restored using probiotic supplements. In total, 999 gut metabolites were detected in the feces, and GDM rats were distinguished from normal rats. The levels of 44 metabolites were increased in GDM rats, and they were alleviated using probiotic supplements. Changes in metabolites in GDM rats were associated with amino acids and bile acids metabolism signaling pathways. Furthermore, changes in metabolites after probiotic supplementation were associated with porphyrin and chlorophyll metabolism pathways. We found that the Allobaculum genus displayed strong positive correlations, whereas the Bryobacter and Gemmatimonas genera displayed strong negative correlations with metabolisms of amino acids and bile acids in GDM rats. The Lactobacillus and Bifidobacterium genera were positively correlated with gut metabolites. Overall, our results showed that metabolism signaling pathways of amino acids and bile acids are associated with the development of GDM. Probiotic supplements alleviate the pathology of GDM through the metabolism pathways of amino acids, bile acids, porphyrin, and chlorophyll.

Comparison of Diagnostic Values of Maternal Arginine Concentration for Different Pregnancy Complications: A Systematic Review and Meta-Analysis

Abnormal arginine metabolism contributes to the development of intrauterine growth restriction (IUGR), preeclampsia (PE), and gestational diabetes mellitus (GDM), which increase the health burden of mothers and induce adverse birth outcomes. However, associations between maternal arginine concentration and different pregnancy complications have not been systematically compared. The PubMed, ScienceDirect, and Web of Science databases were searched for peer-reviewed publications to evaluate the diagnostic value of plasma arginine concentration in complicated pregnancies. Standardized mean difference (SMD) of the arginine concentration was pooled by a random effects model. The results show that increased maternal arginine concentrations were observed in IUGR (SMD: 0.48; 95% CI: 0.20, 0.76; I² = 47.0%) and GDM (SMD: 0.46; 95% CI: 0.11, 0.81; I² = 82.3%) cases but not in PE patients (SMD: 0.21; 95% CI: −0.04, 0.47; I² = 80.3%) compared with the normal cohorts. Subgroup analyses indicated that the non-fasting circulating arginine concentration in third trimester was increased significantly in GDM and severe IUGR pregnancies, but the change mode was dependent on ethnicity. Additionally, only severe PE persons were accompanied by higher plasma arginine concentrations. These findings suggest that maternal arginine concentration is an important reference for assessing the development of pregnancy complications.

Dynamic changes and early predictive value of branched-chain amino acids in gestational diabetes mellitus during pregnancy

OBJECTIVE

Branched-chain amino acids (BCAAs) are closely associated with type 2 diabetes mellitus, but their roles in gestational diabetes mellitus (GDM) are still controversial. This study aims to explore the dynamic changes of BCAAs during pregnancy and identify potential early biomarkers for GDM.

METHODS

This study is a nested case-control study involved 49 women with GDM and 50 age- and body mass index (BMI)-matched healthy pregnant women. The dynamic changes of valine (Val), isoleucine (Ile), and leucine (Leu) were detected in the first (8-12 weeks) and second trimesters (24-28 weeks) by liquid chromatography-mass spectrometry.

RESULTS

Serum Val, Ile, and Leu were higher in GDM patients than in controls in the first trimester. Compared with the first trimester, the serum Val, Ile, and Leu in GDM patients were decreased in the second trimester. In addition, Val, Ile, and Leu in the first trimester were the risk factors for GDM, and Ile presented a high predictive value for GDM. Ile + age (≥ 35) + BMI (≥ 24) exhibited the highest predictive value for GDM (AUC = 0.902, sensitivity = 93.9%, specificity = 80%).

CONCLUSION

Maternal serum Ile in the first trimester was a valuable biomarker for GDM. Ile combined with advanced maternal age and overweight may be used for the early prediction of GDM.

Serum and Amniotic Fluid Metabolic Profile Changes in Response to Gestational Diabetes Mellitus and the Association with Maternal-Fetal Outcomes

This study was designed to identify serum and amniotic fluid (AF) metabolic profile changes in response to gestational diabetes mellitus (GDM) and explore the association with maternal-fetal outcomes. We established the GDM rat models by combining a high-fat diet (HFD) with an injection of low-dose streptozotocin (STZ), detected the fasting plasma glucose (FPG) of pregnant rats in the second and third trimester, and collected AF and fetal rats by cesarean section on gestational day 19 (GD19), as well as measuring the weight and crown-rump length (CRL) of fetal rats. We applied liquid chromatography-tandem mass spectrometry (LC-MS/MS) for the untargeted metabolomics analyses of serum and AF samples and then explored their correlation with maternal-fetal outcomes via the co-occurrence network. The results showed that 91 and 68 metabolites were upregulated and 125 and 78 metabolites were downregulated in serum and AF samples exposed to GDM, respectively. In maternal serum, the obvious alterations emerged in lipids and lipid-like molecules, while there were great changes in carbohydrate and carbohydrate conjugates, followed by amino acids, peptides, and analogs in amniotic fluid. The altered pathways both in serum and AF samples were amino acid, lipid, nucleotide, and vitamin metabolism pathways. In response to GDM, changes in the steroid hormone metabolic pathway occurred in serum, and an altered carbohydrate metabolism pathway was found in AF samples. Among differential metabolites in two kinds of samples, there were 34 common biochemicals shared by serum and AF samples, and a mutual significant association existed. These shared differential metabolites were implicated in several metabolism pathways, including choline, tryptophan, histidine, and nicotinate and nicotinamide metabolism, and among them, N1-methyl-4-pyridone-3-carboxamide, 5'-methylthioadenosine, and kynurenic acid were significantly associated with both maternal FPG and fetal growth. In conclusion, serum and AF metabolic profiles were remarkably altered in response to GDM. N1-Methyl-4-pyridone-3-carboxamide, 5'-methylthioadenosine, and kynurenic acid have the potential to be taken as biomarkers for maternal-fetal health status of GDM. The common and inter-related differential metabolites both in the serum and AF implied the feasibility of predicting fetal health outcomes via detecting the metabolites in maternal serum exposed to GDM.

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.

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

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

Detection of Early Disease Risk Factors Associated with Metabolic Syndrome: A New Era with the NMR Metabolomics Assessment

The metabolic syndrome is a multifactorial disease developed due to accumulation and chronification of several risk factors associated with disrupted metabolism. The early detection of the biomarkers by NMR spectroscopy could be helpful to prevent multifactorial diseases. The exposure of each risk factor can be detected by traditional molecular markers but the current biomarkers have not been enough precise to detect the primary stages of disease. Thus, there is a need to obtain novel molecular markers of pre-disease stages. A promising source of new molecular markers are metabolomics standing out the research of biomarkers in NMR approaches. An increasing number of nutritionists integrate metabolomics into their study design, making nutrimetabolomics one of the most promising avenues for improving personalized nutrition. This review highlight the major five risk factors associated with metabolic syndrome and related diseases including carbohydrate dysfunction, dyslipidemia, oxidative stress, inflammation, and gut microbiota dysbiosis. Together, it is proposed a profile of metabolites of each risk factor obtained from NMR approaches to target them using personalized nutrition, which will improve the quality of life for these patients.