Early markers of gestational diabetes mellitus: what we know and which way forward?

Proteomic analysis of plasma total exosomes and placenta-derived exosomes in patients with gestational diabetes mellitus in the first and second trimesters

Gestational diabetes mellitus (GDM) is the first spontaneous hyperglycemia during pregnancy. Early diagnosis and intervention are important for the management of the disease. This study compared and analyzed the proteins of total plasma exosomes (T-EXO) and placental-derived exosomes (PLAP-EXO) in pregnant women who subsequently developed GDM (12-16 weeks), GDM patients (24-28 weeks) and their corresponding controls to investigate the pathogenesis and biomarkers of GDM associated with exosomes. The exosomal proteins were extracted and studied by proteomics approach, then bioinformatics analysis was applied to the differentially expressed proteins (DEPs) between the groups. At 12-16 and 24-28 weeks of gestation, 36 and 21 DEPs were identified in T-EXO, while 34 and 20 DEPs were identified in PLAP-EXO between GDM and controls, respectively. These proteins are mainly involved in complement pathways, immunity, inflammation, coagulation and other pathways, most of them have been previously reported as blood or exosomal proteins associated with GDM. The findings suggest that the development of GDM is a progressive process and that early changes promote the development of the disease. Maternal and placental factors play a key role in the pathogenesis of GDM. These proteins especially Hub proteins have the potential to become predictive and diagnostic biomarkers for GDM.

Metabolic and Immune Parameters in Pregnant Women with Impaired Glucose Metabolism-A Pilot Study

Gestational diabetes mellitus (GDM) is a public health problem with increasing prevalence. Analyses of metabolic and immune profiles have great potential for discovering new markers and mechanisms related to the development of GDM. We monitored 61 pregnant women during the first and third trimesters of pregnancy, including 13 pregnant women with GDM, 14 pregnant women with elevated glucose in the first trimester and 34 healthy pregnant women. A number of metabolic and immunological parameters were measured, including glucose, insulin, lipid status, fatty acids, lymphocyte profile, adiponectin, IL-6, IL-10 and TNF-a. A higher number of T-helper lymphocytes and a higher ratio of helper/cytotoxic lymphocytes was found in the control group in the first trimester of pregnancy. Pregnant women whose glucose threshold values were measured in the first trimester, but who did not develop GDM, showed a higher percentage of neutrophils and a lower percentage of lymphocytes in the third trimester. Differences in polyunsaturated fatty acids levels were observed between healthy pregnant women and those with glucose metabolism disorders in the first trimester of pregnancy. The results of this pilot study demonstrate that there are differences in the profiles of T lymphocytes, NK cells and polyunsaturated fatty acids between the examined groups of pregnant women, which can serve as a direction for future research.

Novel insights into the genetic architecture of pregnancy glycemic traits from 14,744 Chinese maternities

Glycemic traits are critical indicators of maternal and fetal health during pregnancy. We performed genetic analysis for five glycemic traits in 14,744 Chinese pregnant women. Our genome-wide association study identified 25 locus-trait associations, including established links between gestational diabetes mellitus (GDM) and the genes CDKAL1 and MTNR1B. Notably, we discovered a novel association between fasting glucose during pregnancy and the ESR1 gene (estrogen receptor), which was validated by an independent study in pregnant women. The ESR1-GDM link was recently reported by the FinnGen project. Our work enhances the findings in East Asian populations and highlights the need for independent studies. Further analyses, including genetic correlation, Mendelian randomization, and transcriptome-wide association studies, provided genetic insights into the relationship between pregnancy glycemic traits and hypertension. Overall, our findings advance the understanding of genetic architecture of pregnancy glycemic traits, especially in East Asian populations.

Gut antibiotic resistome during pregnancy associates with the risk of gestational diabetes mellitus: New evidence from a prospective nested case-control study

Antibiotic resistome has emerged as a global threat to public health. However, gestational antibiotic resistome and potential link with adverse pregnancy outcomes remains poorly understood. Our study reports for the first time an association between gut antibiotic resistome during early pregnancy and the risk of gestational diabetes mellitus (GDM) based on a prospective nested case-control cohort including 120 cases and 120 matched controls. A total of 214 antibiotic resistance gene (ARG) subtypes belonging to 17 ARG types were identified in > 10 % fecal samples collected during each trimester. The data revealed dynamic profiles of gut antibiotic resistome through pregnancy, and significant positive associations between selected features (i.e., ARG abundances and a GDM-ARG score which is a new feature characterizing the association between ARGs and GDM) of gut antibiotic resistome during early pregnancy and GDM risk as well as selected endogenous metabolites. The findings demonstrate ubiquitous presence of ARGs in pregnant women and suggest it could constitute an important risk factor for the development of GDM.

SHBG and Insulin resistance - Nexus revisited

Sex hormone binding globulin (SHBG) is a liver-synthesized glycoprotein. Low SHBG levels are associated with insulin resistance (IR). Specific single nucleotide polymorphisms (SNPs) in the SHBG gene are linked to IR. Therefore, it is of interest to provide a review on the comprehensive overview for SHBG related to IR.

Risk factors and diagnostic performance of predictors as a screening technique for gestational diabetes mellitus: a retrospective cross-sectional study

Background

Gestational diabetes mellitus (GDM) is a condition that can have negative impacts on both mother and baby. Detecting GDM early is crucial, and fasting plasma glucose (FPG) has been suggested as a possible screening method. This retrospective cross-sectional study aims to investigate potential risk factors and complications associated with GDM. Additionally, it aims to establish the diagnostic performance of predictive factors as a screening method for GDM.

Methods

Data were collected from the medical records of 247 pregnant women who visited outpatient Obstetrics clinics between 2021 and 2022. The study investigated potential risk factors and complications associated with GDM, including impaired fasting glucose/impaired glucose tolerance (IFG/IGT), family history of diabetes mellitus (DM), and medical conditions. Moreover, the study evaluated the diagnostic performance of potential predictors as screening techniques for GDM.

Results

The study found that IFG/IGT (P<0.001), a history of GDM (P<0.001), and a family history of DM (P=0.022) were significant factors associated with GDM. Healthy individuals had a lower risk of developing GDM (P<0.001). No significant correlation was found between GDM and macrosomia, hypertension, polycystic ovarian syndrome, or other obstetric complications. Although a weak association was observed between fasting blood glucose levels during the first trimester and GDM, it was not significant.

Conclusion

In conclusion, this study found that IFG/IGT and a past history of GDM were significantly associated with GDM. Additionally, a family history of diabetes increased the likelihood of developing GDM, while no significant association was found between GDM and other obstetric complications. Although a weak association was observed between fasting blood glucose levels during the first trimester and GDM, it was not statistically significant.

Hybrid Explainable Artificial Intelligence Models for Targeted Metabolomics Analysis of Diabetic Retinopathy

BACKGROUND

Diabetic retinopathy (DR) is a prevalent microvascular complication of diabetes mellitus, and early detection is crucial for effective management. Metabolomics profiling has emerged as a promising approach for identifying potential biomarkers associated with DR progression. This study aimed to develop a hybrid explainable artificial intelligence (XAI) model for targeted metabolomics analysis of patients with DR, utilizing a focused approach to identify specific metabolites exhibiting varying concentrations among individuals without DR (NDR), those with non-proliferative DR (NPDR), and individuals with proliferative DR (PDR) who have type 2 diabetes mellitus (T2DM).

METHODS

A total of 317 T2DM patients, including 143 NDR, 123 NPDR, and 51 PDR cases, were included in the study. Serum samples underwent targeted metabolomics analysis using liquid chromatography and mass spectrometry. Several machine learning models, including Support Vector Machines (SVC), Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), and Multilayer Perceptrons (MLP), were implemented as solo models and in a two-stage ensemble hybrid approach. The models were trained and validated using 10-fold cross-validation. SHapley Additive exPlanations (SHAP) were employed to interpret the contributions of each feature to the model predictions. Statistical analyses were conducted using the Shapiro-Wilk test for normality, the Kruskal-Wallis H test for group differences, and the Mann-Whitney U test with Bonferroni correction for post-hoc comparisons.

RESULTS

The hybrid SVC + MLP model achieved the highest performance, with an accuracy of 89.58%, a precision of 87.18%, an F1-score of 88.20%, and an F-beta score of 87.55%. SHAP analysis revealed that glucose, glycine, and age were consistently important features across all DR classes, while creatinine and various phosphatidylcholines exhibited higher importance in the PDR class, suggesting their potential as biomarkers for severe DR.

CONCLUSION

The hybrid XAI models, particularly the SVC + MLP ensemble, demonstrated superior performance in predicting DR progression compared to solo models. The application of SHAP facilitates the interpretation of feature importance, providing valuable insights into the metabolic and physiological markers associated with different stages of DR. These findings highlight the potential of hybrid XAI models combined with explainable techniques for early detection, targeted interventions, and personalized treatment strategies in DR management.

Evaluation of preadipocyte factor-1 (Pref-1) level in cord blood of newborns born by mothers with gestational diabetes mellitus (GDM)

BACKGROUND

Gestational diabetes mellitus (GDM) is the most common metabolic complication, which leads to short and long-term consequences in both mother and fetus exposed to hyperglycemia. The aetiology of this condition is proposed to be based on the dysfunction of the adipose tissue, which is characterised by the aberrant generation of adipokines. One of them is preadipocyte factor-1 (Pref-1), which could mediate controlling the adaptation of the maternal metabolism to pregnancy.

AIMS

The study aims to examine the level of Pref-1 in the cord blood of healthy pregnant women's neonates and fetuses born to mothers with GDM.

MATERIALS AND METHODS

Cord blood samples were collected from 30 newborns of mothers with GDM and 40 newborns of healthy pregnant women. Pref-1 concentrations were measured with an ELISA kit.

RESULTS

Fetal Pref-1 concentrations were significantly lower in newborns of mothers with GDM compared to the normal pregnancy group children (5.32 ± 0.29 vs. 7.38 ± 0.53; p < 0.001). Mothers with GDM had a significantly higher index of BMI before pregnancy, maternal gestational weight gain, and maternal fasting glucose. In-depth analysis through multiple variant linear regression revealed a significant association between fetal serum Pref-1 levels, exposure to GDM, and gestational age.

CONCLUSION

These findings contribute valuable insights into maternal-fetal health and pave the way for more targeted and effective clinical interventions.

Future clinical prospects of C-peptide testing in the early diagnosis of gestational diabetes

Gestational diabetes is typically diagnosed in the late second or third trimester of pregnancy. It is one of the most common metabolic disorders among expectant mothers, with potential serious short- and long-term complications for both maternal and offspring health. C-peptide is secreted from pancreatic beta-cells into circulation in equimolar amounts with insulin. It is a useful biomarker to estimate the beta-cell function because it undergoes negligible hepatic clearance and consequently it has a longer half-life compared to insulin. Pregnancy induces increased insulin resistance due to physiological changes in hormonal and metabolic homeostasis. Inadequate compensation by islet beta-cells results in hyperglycemia. The standard oral glucose tolerance test at 24-28 wk of gestation sets the diagnosis. Accumulated evidence from prospective studies indicates a link between early pregnancy C-peptide levels and the risk of subsequent gestational diabetes. Elevated C-peptide levels and surrogate glycemic indices at the beginning of pregnancy could prompt appropriate strategies for secondary prevention.

Serum proteins differentially expressed in gestational diabetes mellitus assessed using isobaric tag for relative and absolute quantitation proteomics

BACKGROUND

As a well-known fact to the public, gestational diabetes mellitus (GDM) could bring serious risks for both pregnant women and infants. During this important investigation into the linkage between GDM patients and their altered expression in the serum, proteomics techniques were deployed to detect the differentially expressed proteins (DEPs) of in the serum of GDM patients to further explore its pathogenesis, and find out possible biomarkers to forecast GDM occurrence.

AIM

To investigation serum proteins differentially expressed in GDM were assessed using isobaric tag for relative and absolute quantitation (iTRAQ) proteomics and bioinformatics analyses.

METHODS

Subjects were divided into GDM and normal control groups according to the IADPSG diagnostic criteria. Serum samples were randomly selected from four cases in each group at 24-28 wk of gestation, and the blood samples were identified by applying iTRAQ technology combined with liquid chromatography-tandem mass spectrometry. Key proteins and signaling pathways associated with GDM were identified by bioinformatics analysis, and the expression of key proteins in serum from 12 wk to 16 wk of gestation was further verified using enzyme-linked immunosorbent assay (ELISA).

RESULTS

Forty-seven proteins were significantly differentially expressed by analyzing the serum samples between the GDM gravidas as well as the healthy ones. Among them, 31 proteins were found to be upregulated notably and the rest 16 proteins were downregulated remarkably. Bioinformatic data report revealed abnormal expression of proteins associated with lipid metabolism, coagulation cascade activation, complement system and inflammatory response in the GDM group. ELISA results showed that the contents of RBP4, as well as ANGPTL8, increased in the serum of GDM gravidas compared with the healthy ones, and this change was found to initiate from 12 wk to 16 wk of gestation.

CONCLUSION

GDM symptoms may involve abnormalities in lipid metabolism, coagulation cascade activation, complement system and inflammatory response. RBP4 and ANGPTL8 are expected to be early predictors of GDM.

Risk Factors of Gestational Diabetes Mellitus Among Women Attending an Antenatal Care Clinic in Prince Sultan Military Medical City (PSMMC), Riyadh, Kingdom of Saudi Arabia: A Case-Control Study

BACKGROUND

Gestational diabetes mellitus (GDM) is a serious health issue for both mother and child. As GDM is common worldwide, healthcare providers pay attention while screening and managing pregnant women to ensure good outcomes for both mother and child.

OBJECTIVE

This study aims to identify the risk factors associated with developing GDM in pregnant women attending antenatal care clinics in Prince Sultan Military Medical City (PSMMC) in Riyadh, Saudi Arabia.

METHODS

This is a case-control study that utilized patients' medical records for data collection. The study included 317 pregnant Saudi women who attended antenatal care clinics and antenatal diabetic clinics in PSMMC from May 2022 to May 2023. Cases were defined as women who met the inclusion and exclusion criteria and had a positive oral glucose tolerance test (OGTT) result, while controls were defined as women in the same age group and gravidity who had negative OGTT. Analysis was conducted using SPSS Statistics version 29.0 (IBM Corp. Released 2021. IBM SPSS Statistics for Windows, Version 29.0. Armonk, NY: IBM Corp.) Results: The total number of cases was 132 out of 313 total samples, representing 42.2% of the total sample. Three factors were associated with an increased risk of developing GDM, including a family history of diabetes (p-value <0.001), a history of GDM (p-value <0.001), and macrosomia (p-value = 0.020). The study also found higher BMI and advanced maternal age were risk factors for GDM (p-value = 0.004, 0.007), respectively. However, the study did not find a significant association between GDM and other factors, such as chronic disease prevalence, history of miscarriage, or history of fetal death.

CONCLUSION

The study identified several risk factors associated with an increased risk of GDM including family history of diabetes, history of GDM, macrosomia, overweight/obesity, and advanced maternal age. It is recommended that antenatal care providers screen for GDM risk factors and closely monitor overweight, obese, or older women. Education and counseling on healthy lifestyle habits, such as maintaining a healthy weight and engaging in physical activity, may also be beneficial for preventing GDM. Further research is needed to confirm and identify additional risk factors for GDM.

Early Gestational Blood Markers to Predict Preeclampsia Complicating Gestational Diabetes Mellitus

Objective

Gestational diabetes mellitus (GDM) and preeclampsia (PE) are common pregnancy complications that share some common risk factors. GDM patients are also at high risk for PE. There are no sensitive markers for prediction, especially for the occurrence of PE in GDM patients. This study investigated plasma proteins for the prediction of PE in GDM patients.

Methods

A total of 10 PE, 10 GDM, and 5 PE complicated with GDM cases, as well as 10 pregnant controls without obvious complications, were included in the nested cohort. The proteomics in the plasma collected at 12-20 weeks of gestational age (GA) were analyzed by liquid chromatography‒mass spectrometry/mass spectrometry. Some potential markers, such as soluble transferrin receptor (sTfR), ceruloplasmin (CP), apolipoprotein E (ApoE) and inositol 1,4,5-trisphosphate receptor 1 (ITPR1), were validated using enzyme-linked immunosorbent assays.

Results

Functional analysis of the plasma showed that proteasome activation, pancreatic secretion, and fatty acid degradation were activated in the GDM group, and renin secretion-, lysosome-, and proteasome pathways involving iron transport and lipid metabolism were enriched in the PE group, distinguishing PE complicating GDM.

Conclusion

Through proteomics analysis of plasma in early pregnancy, PE complicating GDM may have a unique mechanism from that of PE alone. Plasma sTfR, CP and ApoE levels have potential clinical applications in early screening.

Gestational Diabetes Mellitus and Preeclampsia: Correlation and Influencing Factors

Gestational diabetes mellitus (GDM) and preeclampsia (PE) are common pregnancy complications with similar risk factors and pathophysiological changes. Evidence from previous studies suggests that the incidence of PE is significantly increased in women with GDM, but whether GDM is independently related to the occurrence of PE has remained controversial. GDM complicated by PE further increases perinatal adverse events with greater impact on the future maternal and offspring health. Identify factors associated with PE in women with GDM women, specifically those that are controllable, is important for improving pregnancy outcomes. This paper provides the findings of a review on the correlation between GDM and PE, factors associated with PE in women with GDM, possible mechanisms, and predictive markers. Most studies concluded that GDM is independently associated with PE in singleton pregnancy, and optimizing the treatment and management of GDM can reduce the incidence of PE, which is very helpful to improve pregnancy outcomes.