The effects of gestational diabetes mellitus with maternal age between 35 and 40 years on the metabolite profiles of plasma and urine

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.

The effect of gestational diabetes mellitus on pregnancy outcomes in advanced primiparous women: A retrospective study

Gestational diabetes mellitus (GDM) could have a variable degree of adverse effects on pregnancy outcomes for both pregnant women and newborns. The purpose of the study was to explore the effect of GDM on pregnancy outcomes in advanced primiparous women. A total of 1076 advanced primiparous women were included between January 2020 and December 2022. All these women were divided into the GDM group (n = 434) and the non-GDM group (n = 642). Variables included baseline characteristics, maternal, and newborn outcomes were collected. The risk of each adverse outcome was analyzed by multivariate logistic regression models. The effect of blood glucose control on pregnancy outcomes was further analyzed among GDM women with good glycaemic control (n = 381) and poor glycaemic control (n = 53). Analysis of baseline characteristics demonstrated a significant difference in prepregnancy body mass index (median, IQR: 22.27 [20.58-24.44] vs 21.17 [19.53-22.86], P < .01) between the GDM group and the non-GDM group. A significantly higher incidence rate of adverse pregnancy outcomes was found in advanced primiparous women with GDM, such as polyhydramniosis, premature birth, low-birth weight, macrosomia, and neonatal intensive care unit admission (all P < .05). Compared with the non-GDM group, the risk of polyhydramniosis was nearly twice as high in the GDM group (adjusted odds ratio: 1.94, 95% confidence interval: 1.01-3.72, P = .04) after adjusted baseline characteristics. Among the GDM group, the women with poor glycaemic control showed a significantly higher incidence rate of polyhydramnios, hypertensive disorders of pregnancy, cesarean delivery, premature birth, low-birth weight, macrosomia, and neonatal intensive care unit admission was significant than the women with good glycaemic control (all P < .05). GDM was an independent risk factor for polyhydramnios in advanced primiparous women. At the same time, good glycaemic control in diabetics advanced primiparous women could reduce adverse pregnancy outcomes.

The role of lipid dysregulation in gestational diabetes mellitus: Early prediction and postpartum prognosis

Gestational diabetes mellitus (GDM) is a pathological condition during pregnancy characterized by impaired glucose tolerance, and the failure of pancreatic beta-cells to respond appropriately to an increased insulin demand. However, while the majority of women with GDM will return to normoglycemia after delivery, they have up to a seven times higher risk of developing type 2 diabetes during midlife, compared with those with no history of GDM. Gestational diabetes mellitus also increases the risk of multiple metabolic disorders, including non-alcoholic fatty liver disease, obesity, and cardiovascular diseases. Lipid metabolism undergoes significant changes throughout the gestational period, and lipid dysregulation is strongly associated with GDM and the progression to future type 2 diabetes. In addition to common lipid variables, discovery-based omics techniques, such as metabolomics and lipidomics, have identified lipid biomarkers that correlate with GDM. These lipid species also show considerable potential in predicting the onset of GDM and subsequent type 2 diabetes post-delivery. This review aims to update the current knowledge of the role that lipids play in the onset of GDM, with a focus on potential lipid biomarkers or metabolic pathways. These biomarkers may be useful in establishing predictive models to accurately predict the future onset of GDM and type 2 diabetes, and early intervention may help to reduce the complications associated with GDM.

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.

An early prediction model for gestational diabetes mellitus based on metabolomic biomarkers

BACKGROUND

Gestational diabetes mellitus (GDM) represents the main metabolic alteration during pregnancy. The available methods for diagnosing GDM identify women when the disease is established, and pancreatic beta-cell insufficiency has occurred.The present study aimed to generate an early prediction model (under 18 weeks of gestation) to identify those women who will later be diagnosed with GDM.

METHODS

A cohort of 75 pregnant women was followed during gestation, of which 62 underwent normal term pregnancy and 13 were diagnosed with GDM. Targeted metabolomics was used to select serum biomarkers with predictive power to identify women who will later be diagnosed with GDM.

RESULTS

Candidate metabolites were selected to generate an early identification model employing a criterion used when performing Random Forest decision tree analysis. A model composed of two short-chain acylcarnitines was generated: isovalerylcarnitine (C5) and tiglylcarnitine (C5:1). An analysis by ROC curves was performed to determine the classification performance of the acylcarnitines identified in the study, obtaining an area under the curve (AUC) of 0.934 (0.873-0.995, 95% CI). The model correctly classified all cases with GDM, while it misclassified ten controls as in the GDM group. An analysis was also carried out to establish the concentrations of the acylcarnitines for the identification of the GDM group, obtaining concentrations of C5 in a range of 0.015-0.25 μmol/L and of C5:1 with a range of 0.015-0.19 μmol/L.

CONCLUSION

Early pregnancy maternal metabolites can be used to screen and identify pregnant women who will later develop GDM. Regardless of their gestational body mass index, lipid metabolism is impaired even in the early stages of pregnancy in women who develop GDM.

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.