Perspectives from metabolomics in the early diagnosis and prognosis of gestational diabetes mellitus

Unravelling the Metabolic Underpinnings of Gestational Diabetes Mellitus: A Comprehensive Mendelian Randomisation Analysis Identifying Causal Metabolites and Biological Pathways

BACKGROUND

Gestational diabetes mellitus (GDM) has a strong genetic predisposition. Integrating metabolomics with Mendelian randomisation (MR) analysis offers a potent method to uncover the metabolic factors causally linked to GDM pathogenesis.

OBJECTIVES

This study aims to identify specific metabolites and metabolic pathways causally associated with GDM susceptibility through a comprehensive MR analysis. Additionally, it seeks to explore the potential of these identified metabolites as circulating biomarkers for early GDM detection and risk assessment. Furthermore, it aims to evaluate the implicated metabolic pathways as potential therapeutic targets for preventive or interventional strategies against GDM.

METHODS

A two-sample MR study was conducted using summary statistics from a metabolite genome-wide association study (GWAS) of 8299 individuals and a GDM GWAS comprising 13,039 cases and 197,831 controls. Rigorous criteria were applied to select robust genetic instruments for 850 metabolites.

RESULTS

MR analysis revealed 47 metabolites exhibiting putative causal associations with GDM risk. Among these, five metabolites demonstrated statistically significant associations after multiple-testing correction: Beta-citrylglutamate, Isobutyrylcarnitine (c4), 1,2-dilinoleoyl-GPC (18:2/18:2), Alliin and Cis-3,4-methyleneheptanoylcarnitine. Importantly, all these metabolites exhibited protective effects against GDM development. Additionally, metabolic pathway enrichment analysis implicated the methionine metabolism and spermidine and spermine biosynthesis pathways in the pathogenesis of GDM.

CONCLUSION

This comprehensive MR study has robustly identified specific metabolites and metabolic pathways with causal links to GDM susceptibility. These findings provide novel insights into the metabolic underpinnings of GDM aetiology and offer promising translational implications. The identified metabolites could serve as potential circulating biomarkers for early detection and risk stratification, while the implicated metabolic pathways may represent therapeutic targets for preventive or interventional strategies against GDM.

A review of disease risk prediction methods and applications in the omics era

Risk prediction and disease prevention are the innovative care challenges of the 21st century. Apart from freeing the individual from the pain of disease, it will lead to low medical costs for society. Until very recently, risk assessments have ushered in a new era with the emergence of omics technologies, including genomics, transcriptomics, epigenomics, proteomics, and so on, which potentially advance the ability of biomarkers to aid prediction models. While risk prediction has achieved great success, there are still some challenges and limitations. We reviewed the general process of omics-based disease risk model construction and the applications in four typical diseases. Meanwhile, we highlighted the problems in current studies and explored the potential opportunities and challenges for future clinical practice.

Metabolomic profiling reveals decreased serum cysteine levels during gestational diabetes mellitus progression

Gestational diabetes mellitus (GDM) is a pregnancy-related metabolic disorder associated with short-term and long-term adverse health outcomes, but its pathogenesis has not been clearly elucidated. Investigations of the dynamic changes in metabolomic markers in different trimesters may reveal the underlying pathophysiology of GDM progression. Therefore, in the present study, we analysed the metabolic profiles of 75 women with GDM and 75 women with normal glucose tolerance throughout the three trimesters. We found that the variation trends of 38 metabolites were significantly changed during GDM development. Specifically, longitudinal analyses revealed that cysteine (Cys) levels significantly decreased over the course of GDM progression. Further study showed that Cys alleviated GDM in female mice at gestational day 14.5, possibly by inhibiting phosphoenolpyruvate carboxykinase to suppress hepatic gluconeogenesis. Taken together, these findings suggest that the Cys metabolism pathway might play a crucial role in GDM and Cys supplementation represents a potential new treatment strategy for GDM patients.

Triple-Responsive, Multimodal, Visual Electronic Skin toward All-in-One Health Management for Gestational Diabetes Mellitus

Gestational diabetes mellitus (GDM) is one of the most common metabolic disorders during pregnancy, leading to serious complications for pregnant women and a threat to life safety of infants. Therefore, it is particularly important to establish a multipurpose monitoring pathway to important physiological indicators of pregnant women. In this work, three kinds of double network hydrogels are prepared with poly(vinyl alcohol) (PVA), borax, and cellulose ethers with varying substituents of methyl (methyl cellulose, MC), hydroxypropyl (hydroxypropyl cellulose, HPC), or both (hydroxypropyl methyl cellulose, HPMC), respectively. The corresponding toughness (143.9, 102.3, and 135.9 kJ cm-3) and conductivity (0.69, 0.45, and 0.51 S m-1) of the hydrogels demonstrate that PB-MC was endowed with the prominent performance. Molecular dynamics simulations further revealed the essence that hydrogen bond interactions between PVA and cellulose ethers play a critical role in regulating the structure and properties of hydrogels. Thermochromic capsule powders (TCPs) were subsequently doped in to achieve a composite hydrogel (TCPs@PB-MC) to indicate the change in human body temperature. Furthermore, the process of the TCPs@PB-MC response to glucose, pH, and temperature was tracked in-depth through the electrochemical window. This work provides a novel strategy for all-in-one health management of GDM.

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

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

Predictive value of first-trimester GPR120 levels in gestational diabetes mellitus

Background

Early diagnosis of gestational diabetes mellitus (GDM) reduces the risk of unfavorable perinatal and maternal consequences. Currently, there are no recognized biomarkers or clinical prediction models for use in clinical practice to diagnosing GDM during early pregnancy. The purpose of this research is to detect the serum G-protein coupled receptor 120 (GPR120) levels during early pregnancy and construct a model for predicting GDM.

Methods

This prospective cohort study was implemented at the Women's Hospital of Jiangnan University between November 2019 and November 2022. All clinical indicators were assessed at the Hospital Laboratory. GPR120 expression was measured in white blood cells through quantitative PCR. Thereafter, the least absolute shrinkage and selection operator (LASSO) regression analysis technique was employed for optimizing the selection of the variables, while the multivariate logistic regression technique was implemented for constructing the nomogram model to anticipate the risk of GDM. The calibration curve analysis, area under the receiver operating characteristic curve (AUC) analysis, and the decision curve analysis (DCA) were conducted for assessing the performance of the constructed nomogram.

Results

Herein, we included a total of 250 pregnant women (125 with GDM). The results showed that the GDM group showed significantly higher GPR120 expression levels in their first trimester compared to the normal pregnancy group (p < 0.05). LASSO and multivariate regression analyses were carried out to construct a GDM nomogram during the first trimester. The indicators used in the nomogram included fasting plasma glucose, total cholesterol, lipoproteins, and GPR120 levels. The nomogram exhibited good performance in the training (AUC 0.996, 95% confidence interval [CI] = 0.989-0.999) and validation sets (AUC=0.992) for predicting GDM. The Akaike Information Criterion of the nomogram was 37.961. The nomogram showed a cutoff value of 0.714 (sensitivity = 0.989; specificity = 0.977). The nomogram displayed good calibration and discrimination, while the DCA was conducted for validating the clinical applicability of the nomogram.

Conclusions

The patients in the GDM group showed a high GPR120 expression level during the first trimester. Therefore, GPR120 expression could be used as an effective biomarker for predicting the onset of GDM. The nomogram incorporating GPR120 levels in early pregnancy showed good predictive ability for the onset of GDM.

Utilizing Amniotic Fluid Metabolomics to Monitor Fetal Well-Being: A Narrative Review of the Literature

Fetal and perinatal periods are critical phases for long-term development. Early diagnosis of maternal complications is challenging due to the great complexity of these conditions. In recent years, amniotic fluid has risen in a prominent position in the latest efforts to describe and characterize prenatal development. Amniotic fluid may provide real-time information on fetal development and metabolism throughout pregnancy as substances from the placenta, fetal skin, lungs, gastric fluid, and urine are transferred between the mother and the fetus. Applying metabolomics to monitor fetal well-being, in such a context, could help in the understanding, diagnosis, and treatment of these conditions and is a promising area of research. This review shines a spotlight on recent amniotic fluid metabolomics studies and their methods as an interesting tool for the assessment of many conditions and the identification of biomarkers. Platforms in use, such as proton nuclear magnetic resonance (¹H NMR) and ultra-high-performance liquid chromatography (UHPLC), have different merits, and a combinatorial approach could be valuable. Metabolomics may also be used in the quest for habitual diet-induced metabolic signals in amniotic fluid. Finally, analysis of amniotic fluid can provide information on exposure to exogenous substances by detecting the exact levels of metabolites carried to the fetus and associated metabolic effects.

Exploring preconception signatures of metabolites in mothers with gestational diabetes mellitus using a non-targeted approach

BACKGROUND

Metabolomic changes during pregnancy have been suggested to underlie the etiology of gestational diabetes mellitus (GDM). However, research on metabolites during preconception is lacking. Therefore, this study aimed to investigate distinctive metabolites during the preconception phase between GDM and non-GDM controls in a nested case-control study in Singapore.

METHODS

Within a Singapore preconception cohort, we included 33 Chinese pregnant women diagnosed with GDM according to the IADPSG criteria between 24 and 28 weeks of gestation. We then matched them with 33 non-GDM Chinese women by age and pre-pregnancy body mass index (ppBMI) within the same cohort. We performed a non-targeted metabolomics approach using fasting serum samples collected within 12 months prior to conception. We used generalized linear mixed model to identify metabolites associated with GDM at preconception after adjusting for maternal age and ppBMI. After annotation and multiple testing, we explored the additional predictive value of novel signatures of preconception metabolites in terms of GDM diagnosis.

RESULTS

A total of 57 metabolites were significantly associated with GDM, and eight phosphatidylethanolamines were annotated using HMDB. After multiple testing corrections and sensitivity analysis, phosphatidylethanolamines 36:4 (mean difference β: 0.07; 95% CI: 0.02, 0.11) and 38:6 (β: 0.06; 0.004, 0.11) remained significantly higher in GDM subjects, compared with non-GDM controls. With all preconception signals of phosphatidylethanolamines in addition to traditional risk factors (e.g., maternal age and ppBMI), the predictive value measured by area under the curve (AUC) increased from 0.620 to 0.843.

CONCLUSIONS

Our data identified distinctive signatures of GDM-associated preconception phosphatidylethanolamines, which is of potential value to understand the etiology of GDM as early as in the preconception phase. Future studies with larger sample sizes among alternative populations are warranted to validate the associations of these signatures of metabolites and their predictive value in GDM.