Identification of proteomic biomarkers in maternal plasma in the early second trimester that predict the subsequent development of gestational diabetes

Mass spectrometry-based "omics" technologies for the study of gestational diabetes and the discovery of new biomarkers

Gestational diabetes (GDM) is one of the most common complications occurring during pregnancy. Diagnosis is performed by oral glucose tolerance test, but harmonized testing methods and thresholds are still lacking worldwide. Short-term and long-term effects include obesity, type 2 diabetes, and increased risk of cardiovascular disease. The identification and validation of sensitidve, selective, and robust biomarkers for early diagnosis during the first trimester of pregnancy are required, as well as for the prediction of possible adverse outcomes after birth. Mass spectrometry (MS)-based omics technologies are nowadays the method of choice to characterize various pathologies at a molecular level. Proteomics and metabolomics of GDM were widely investigated in the last 10 years, and various proteins and metabolites were proposed as possible biomarkers. Metallomics of GDM was also reported, but studies are limited in number. The present review focuses on the description of the different analytical methods and MS-based instrumental platforms applied to GDM-related omics studies. Preparation procedures for various biological specimens are described and results are briefly summarized. Generally, only preliminary findings are reported by current studies and further efforts are required to determine definitive GDM biomarkers.

Predictive plasma biomarker for gestational diabetes: A case-control study in China

OBJECTIVE

This study aims to find new plasma biomarkers in early pregnancy.

DESIGN

The original study enrolled 1219 pregnant women. We investigated protein expression profiles of placental tissues from women with GDM (n = 89) and normal glucose tolerance (NGT) (n = 83). Maternal plasma samples between two groups in early and middle pregnancy were used for validation of candidate biomarkers.

METHODS

Differentially expressed proteins (DEPs) were identified by label-free quantitative proteomics from human placenta samples between two groups. Several DEPs were validated in plasma by Luminex assays. An automatic biochemical analyzer was used to detect blood lipid indexes. The associations of GAL-3BP with biochemical indicators were demonstrated by Pearson's correlation analysis. Binary logistic regression was used to model potential predictive indicators in early pregnancy of GDM. Receiver operating characteristic (ROC) curve was used to evaluate the diagnostic accuracy of the predictive model and the value of GAL-3BP.

RESULTS

123 DEPs were found in placenta involved in ribosomal function, pancreatic secretion, oxidative phosphorylation, and inflammatory signaling pathway. Plasma GAL-3BP are significantly higher in women with GDM than NGT in the first (p = 0.008) and second (p = 0.026) trimester, but C9 and VWF have no difference. The predictive value of GAL-3BP in the first trimester of pregnancy (AUC 0.64) is better than that in the second trimester (AUC 0.61), and combined predictive model of TG and GAL-3BP at early pregnancy has greater predictive and diagnostic value for GDM (AUC 0.69) than individual GAL-3BP (AUC 0.64).

CONCLUSIONS

Plasma TG and GAL-3BP has good predictive and diagnostic value at early pregnancy, suggesting that these two indicators may be used as biomarkers for early prediction and diagnosis of GDM.

SIGNIFICANCE

The advantage of this study is that circulating TG and GAL-3BP might differentiate the progress of women with GDM and normal glucose tolerance (NGT) at the early stage of pregnancy. It is the first study to consider the role of GAL-3BP as an early predictive biomarker in the development of GDM during the whole pregnancy. Another advantage is that volunteers in this study were recruited from two provinces in China to eliminate the impacts of environmental confounders. The similar changes of blood glucose/lipid indicators for women with GDM and NGT in both regions was found in the first and second trimester of pregnancy, which added to the reliability of analytical results.

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.

Proteomics Studies in Gestational Diabetes Mellitus: A Systematic Review and Meta-Analysis

Gestational Diabetes Mellitus (GDM) is the most common metabolic complication during pregnancy and is associated with serious maternal and fetal complications such as pre-eclampsia and stillbirth. Further, women with GDM have approximately 10 times higher risk of diabetes later in life. Children born to mothers with GDM also face a higher risk of childhood obesity and diabetes later in life. Early prediction/diagnosis of GDM leads to early interventions such as diet and lifestyle, which could mitigate the maternal and fetal complications associated with GDM. However, no biomarkers identified to date have been proven to be effective in the prediction/diagnosis of GDM. Proteomic approaches based on mass spectrometry have been applied in various fields of biomedical research to identify novel biomarkers. Although a number of proteomic studies in GDM now exist, a lack of a comprehensive and up-to-date meta-analysis makes it difficult for researchers to interpret the data in the existing literature. Thus, we undertook a systematic review and meta-analysis on proteomic studies and GDM. We searched MEDLINE, EMBASE, Web of Science and Scopus from inception to January 2022. We searched Medline, Embase, CINHAL and the Cochrane Library, which were searched from inception to February 2021. We included cohort, case-control and observational studies reporting original data investigating the development of GDM compared to a control group. Two independent reviewers selected eligible studies for meta-analysis. Data collection and analyses were performed by two independent reviewers. The PROSPERO registration number is CRD42020185951. Of 120 articles retrieved, 24 studies met the eligibility criteria, comparing a total of 1779 pregnant women (904 GDM and 875 controls). A total of 262 GDM candidate biomarkers (CBs) were identified, with 49 CBs reported in at least two studies. We found 22 highly replicable CBs that were significantly different (nine CBs were upregulated and 12 CBs downregulated) between women with GDM and controls across various proteomic platforms, sample types, blood fractions and time of blood collection and continents. We performed further analyses on blood (plasma/serum) CBs in early pregnancy (first and/or early second trimester) and included studies with more than nine samples (nine studies in total). We found that 11 CBs were significantly upregulated, and 13 CBs significantly downregulated in women with GDM compared to controls. Subsequent pathway analysis using Database for Annotation, Visualization and Integrated Discovery (DAVID) bioinformatics resources found that these CBs were most strongly linked to pathways related to complement and coagulation cascades. Our findings provide important insights and form a strong foundation for future validation studies to establish reliable biomarkers for GDM.

The Amniotic Fluid Proteome Differs Significantly between Donor and Recipient Fetuses in Pregnancies Complicated by Twin-to-Twin Transfusion Syndrome

BACKGROUND

Twin-to-twin transfusion syndrome (TTTS) is a serious complication of monochorionic twin pregnancies. It results from disproportionate blood supply to each fetus caused by abnormal vascular anastomosis within the placenta. Amniotic fluid (AF) is an indicator reflecting the various conditions of the fetus, and an imbalance in AF volume is essential for the antenatal diagnosis of TTTS by ultrasound. In this study, two different mass spectrometry quantitative approaches were performed to identify differentially expressed proteins (DEPs) within matched pairs of AF samples.

METHODS

We characterized the AF proteome in pooled AF samples collected from donor and recipient twin pairs (n = 5 each) with TTTS by a global proteomics profiling approach and then preformed the statistical analysis to determine the DEPs between the two groups. Next, we carried out a targeted proteomic approach (multiple reaction monitoring) with DEPs to achieve high-confident TTTS-associated AF proteins.

RESULTS

A total of 103 AF proteins that were significantly altered in their abundances between donor and recipient fetuses. The majority of upregulated proteins identified in the recipient twins (including carbonic anhydrase 1, fibrinogen alpha chain, aminopeptidase N, alpha-fetoprotein, fibrinogen gamma chain, and basement membrane-specific heparan sulfate proteoglycan core protein) have been associated with cardiac or dermatologic disease, which is often seen in recipient twins as a result of volume overload. In contrast, proteins significantly upregulated in AF collected from donor twins (including IgGFc-binding protein, apolipoprotein C-I, complement C1q subcomponent subunit B, apolipoprotein C-III, apolipoprotein A-II, decorin, alpha-2-macroglobulin, apolipoprotein A-I, and fibronectin) were those previously shown to be associated with inflammation, ischemic cardiovascular complications or renal disease.

CONCLUSION

In this study, we identified proteomic biomarkers in AF collected from donor and recipient twins in pregnancies complicated by TTTS that appear to reflect underlying functional and pathophysiological challenges faced by each of the fetuses.

A Critical Review of Proteomic Studies in Gestational Diabetes Mellitus

Gestational diabetes mellitus is a progressive and complex pregnancy complication, which threatens both maternal and fetal health. It is urgent to screen for specific biomarkers for early diagnosis and precise treatment, as well as to identify key moleculars to better understand the pathogenic mechanisms. In the present review, we comprehensively summarized recent studies of gestational diabetes using mass spectrometry-based proteomic technologies. Focused on the entire experimental design and proteomic results, we showed that these studies have covered a broad range of research contents in terms of sampling time, sample types, and outcome associations. Although most of the studies only stayed in the stage of initial discovery, several proteins were further verified to be efficient for disease diagnosis. Functional analysis of all the combined significant proteins also showed that a small number of proteins are known to be involved in the regulation of insulin or indirect signaling pathways. However, many factors such as diagnostic criteria, sample processing, proteomic method, and statistical method can greatly affect the identification of reproducible and reliable protein candidates. Thus, we further provided constructive suggestions and recommendations for carrying out proteomic or follow-up studies of gestational diabetes or other pregnancy complications in the future.

Identification of urinary biomarkers for the prediction of gestational diabetes mellitus in early second trimester of young gravidae based on iTRAQ quantitative proteomics

Gestational Diabetes Mellitus (GDM) has brought great harm to maternal and fetus. Up to now, only a few plasma biomarkers for its early diagnosis have been reported; nevertheless, there is no report about identification of urinary biomarkers for prediction of GDM. Thus, it is necessary to correct this deficiency. In our study, urine samples were collected from 889 healthy young gravidae at the early second trimester (15 to 20 weeks), 69 of whom were subsequently diagnosed with GDM at 24 to 28 weeks. iTRAQ (the isobaric tags for relative and absolute quantification) quantitative proteomics was conducted on sixteen GDM (trial group) and an equal number of matched healthy young gravidae (control group). Validation was performed in 40 cases of each group by ELISA. A total of 1,901 proteins were identified in this study, including 119 significantly differential proteins (fold change ≧1.2 or ≦0.83 and p < 0.05). Compared with control group, 83 differential proteins were increased and 36 proteins were decreased in GDM group. The validation for expression of CD59 and IL1RA showed significant difference and the area under the receiver operating characteristic curve was 0.729 and 0.899, respectively (p < 0.05). The two candidate protein biomarkers (CD59 and IL1RA) in urine could be an early, noninvasive diagnostic predictors of young pravidae with GDM, and IL1RA is stronger diagnostic power than CD59.

Gestational diabetes from A to Z

Gestational diabetes mellitus (GDM) is defined as any degree of hyperglycaemia that is recognized for the first time during pregnancy. This definition includes cases of undiagnosed type 2 diabetes mellitus (T2DM) identified early in pregnancy and true GDM which develops later. GDM constitutes a greater impact on diabetes epidemic as it carries a major risk of developing T2DM to the mother and foetus later in life. In addition, GDM has also been linked with cardiometabolic risk factors such as lipid abnormalities, hypertensive disorders and hyperinsulinemia. These might result in later development of cardiovascular disease and metabolic syndrome. The understanding of the different risk factors, the pathophysiological mechanisms and the genetic factors of GDM, will help us to identify the women at risk, to develop effective preventive measures and to provide adequate management of the disease. Clinical trials have shown that T2DM can be prevented in women with prior GDM, by intensive lifestyle modification and by using pioglitazone and metformin. However, a matter of controversy surrounding both screening and management of GDM continues to emerge, despite several recent well-designed clinical trials tackling these issues. The aim of this manuscript is to critically review GDM in a detailed and comprehensive manner, in order to provide a scientific analysis and updated write-up of different related aspects.

First-trimester multimarker prediction of gestational diabetes mellitus using targeted mass spectrometry

AIMS/HYPOTHESIS

Gestational diabetes mellitus (GDM) is associated with an increased risk of pre-eclampsia, macrosomia and the future development of type 2 diabetes mellitus in both mother and child. Although an early and accurate prediction of GDM is needed to allow intervention and improve perinatal outcome, no single protein biomarker has yet proven useful for this purpose. In the present study, we hypothesised that multimarker panels of serum proteins can improve first-trimester prediction of GDM among obese and non-obese women compared with single markers.

METHODS

A nested case-control study was performed on first-trimester serum samples from 199 GDM cases and 208 controls, each divided into an obese group (BMI ≥27 kg/m(2)) and a non-obese group (BMI <27 kg/m(2)). Based on their biological relevance to GDM or type 2 diabetes mellitus or on their previously reported potential as biomarkers for these diseases, a number of proteins were selected for targeted nano-flow liquid chromatography (LC) MS analysis. This resulted in the development and validation of a 25-plex multiple reaction monitoring (MRM) MS assay.

RESULTS

After false discovery rate correction, six proteins remained significantly different (p<0.05) between obese GDM patients (n=135) and BMI-matched controls (n=139). These included adiponectin, apolipoprotein M and apolipoprotein D. Multimarker models combining protein levels and clinical data were then constructed and evaluated by receiver operating characteristic (ROC) analysis. For the obese, non-obese and all GDM groups, these models achieved marginally higher AUCs compared with adiponectin alone.

CONCLUSIONS/INTERPRETATION

Multimarker models combining protein markers and clinical data have the potential to predict women at a high risk of developing GDM.

A Combined Metabolomic and Proteomic Analysis of Gestational Diabetes Mellitus

The aim of this pilot study was to apply a novel combined metabolomic and proteomic approach in analysis of gestational diabetes mellitus. The investigation was performed with plasma samples derived from pregnant women with diagnosed gestational diabetes mellitus (n = 18) and a matched control group (n = 13). The mass spectrometry-based analyses allowed to determine 42 free amino acids and low molecular-weight peptide profiles. Different expressions of several peptides and altered amino acid profiles were observed in the analyzed groups. The combination of proteomic and metabolomic data allowed obtaining the model with a high discriminatory power, where amino acids ethanolamine, L-citrulline, L-asparagine, and peptide ions with m/z 1488.59; 4111.89 and 2913.15 had the highest contribution to the model. The sensitivity (94.44%) and specificity (84.62%), as well as the total group membership classification value (90.32%) calculated from the post hoc classification matrix of a joint model were the highest when compared with a single analysis of either amino acid levels or peptide ion intensities. The obtained results indicated a high potential of integration of proteomic and metabolomics analysis regardless the sample size. This promising approach together with clinical evaluation of the subjects can also be used in the study of other diseases.

Proteomic-driven biomarker discovery in gestational diabetes mellitus: a review

Gestational diabetes mellitus (GDM) is defined as any degree of glucose intolerance with onset or first recognition during pregnancy and it affects 18% of pregnant women worldwide. GDM is considered a high-risk state which may lead to type II diabetes which is associated with an increase in a number of interrelated adverse perinatal outcomes. Given the fact that the progress of a successful pregnancy is dependent on the intricate communication between several biological molecules, identification of the proteomic profile perturbations in women with GDM is expected to help in understanding the disease pathogenesis and also discovery of clinical biomarker(s). In recent years, both gel-free and gel-based proteomics have been extensively investigated for improving maternal and child health. Although there are several reports integrating various aspects of proteomics in pregnancy related diseases such as preeclampsia, extensive Pubmed search shows no review so far on the application of proteomics in gestational diabetes. In this review, we focus on various high-throughput proteomic technologies for the identification of unique biosignatures and biomarkers responsible for the early prediction of GDM. Further, different analytical strategies and biological samples involved in proteomic analysis of this pregnancy-related disease are discussed.This article is part of a Special Issue entitled: Proteomics in India.

The application of fuzzy statistics and linear discriminant analysis as criteria for optimizing the preparation of plasma for matrix-assisted laser desorption/ionization mass spectrometry peptide profiling

An alternative bioinformatics approach based on fuzzy theory statistics and linear discriminant analysis is proposed for the interpretation of MALDI MS spectra in peptide profiling. When applied, the methodology enables the establishment of a reproducible plasma preparation protocol appropriate for the evaluation of small data sets. The samples were collected from pregnant women affected by gestational diabetes mellitus (GDM), n=18 and control group, n=13. The following pre-treatment sets were tested: pipette tips with C18 stationary phase (ZipTip, Millipore and Omix, Agilent) and magnetic bead-based weak cation exchange chromatography kit (MB WCX, Bruker Daltonics). The spectra were recorded using a MALDI TOF mass spectrometer (UltrafleXtreme, Bruker Daltonics) for a mass range of m/z from 1000 to 10,000. The significant features were selected using the wrapper selection method, and two classification systems were tested: discriminant analysis (DA) and fuzzy inference system (FIS). ClinProTools software was employed to compare the usefulness of the proposed methodology. The study showed that the optimum results for MS spectra were obtained after the use of the ZipTip as pre-treatment method in plasma preparation. Chemometric analysis allowed the differentiation of the GDM group from the control with a high degree of accuracy: 0.7333 (DA) and 0.8065 (FIS).

Lack of predictive power of plasma lipids or lipoproteins for gestational diabetes mellitus in Japanese women

Aims/Introduction

To determine the diagnostic potential of plasma lipids and apolipoproteins in gestational diabetes mellitus (GDM), we carried out a retrospective cohort study of 1,161 Japanese women at 20–28 weeks of gestation who underwent a glucose challenge test (GCT).

Materials and Methods

A total of 1,161 Japanese women at 20–28 weeks of gestation underwent a GCT. Participants with a positive test (GCT[+]) underwent a subsequent oral glucose tolerance test. Clinical and biochemical parameters were determined and quantification of apolipoproteins (Apo), including ApoB, ApoB48, ApoA-I and ApoC-III, was carried out.

Results

The prevalence of GCT(+; with a 130 mg/dL glucose cut-off) and GDM was 20% and 4%, respectively. There was a trend for increased triglycerides and ApoC-III in GDM(+) participants. However, the difference in plasma triglycerides, ApoC-III or ApoB48 did not reach statistical significance between GDM(+) and GDM(−) women. Values of 1-h glucose (P < 0.001) and fasting glucose (P = 0.002) were significant risk factors for GDM.

Conclusions

Prediction of GDM using only the ApoC-III value is not easy, although triglycerides and ApoC-III were higher in the GDM(+) group. The present findings show no significant difference in plasma lipid levels between women diagnosed with GDM and those with normal glucose tolerance.

Magnetic Bead-Based Serum Peptidome Profiling in Patients with Gestational Diabetes Mellitus

Gestational diabetes mellitus (GDM) is a frequent medical condition during pregnancy. Early diagnosis and treatment of GDM are crucial for both the mother and the baby. In the present study, we aimed to identify specific biomarkers to assist in the early detection of GDM and give some clues to the possible causes of GDM by comparing serum peptide profile differences between GDM patients and healthy controls. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) was used in combination with weak cation exchange magnetic bead (WCX-MB). Levels of four peptides (4418.9, 2219.7, 2211.5, and 1533.4 Da) were significantly different. Interestingly, three of them (4418.9, 2211.5, and 1533.4 Da) were identified when GDM patients with two degrees of glucose intolerance were compared. Additionally, peptides 2211.5 and 1533.4 Da showed a decreasing trend as glucose intolerance increased, while peptide 4418.9 Da exhibited the reverse tendency. In conclusion, our study provides novel insights into the altered serum peptide profile of GDM patients. The specific candidate biomarkers may contribute to the development of GDM.

Prediction of gestational diabetes early in pregnancy: targeting the long-term complications

Gestational diabetes (GD), defined as carbohydrate intolerance with onset or first recognition during pregnancy, has a prevalence of 7% and is a growing problem worldwide. Infants born to mothers with GD are more likely to be large for gestational age, incur traumatic birth injury, require a stay in the intensive care unit and develop postnatal metabolic disturbances. As the worldwide epidemic of obesity worsens, more women are entering pregnancy with metabolic alterations and preexisting insulin resistance, which is heightened by the hormonal milieu of pregnancy. The Hyperglycemia Adverse Pregnancy Outcome (HAPO) study has clearly shown that GD-related complications correlate with glycemic control. We will review the current understanding of the physiology of GD and the screening and treatment guidelines that are commonly utilized in clinical care. In addition, we will discuss the need for development of multiparametric models combining maternal clinical risk factors and biomarkers early in pregnancy to better stratify and predict risk of GD-related complications and offer targeted intervention.

Human serum proteome analysis: new source of markers in metabolic disorders

The prevalence of metabolic disorders (MDs), especially diabetes, is rapidly increasing worldwide, leading to an increasing risk of cardiovascular and other socially relevant complications. To boost MD biomarker discovery, advanced proteomics can harmonize metabolomics. Indeed, the rapid development of mass spectrometry (MS) has designated proteomics as an emerging platform to interrogate the plasma/serum proteome for the discovery of next-generation biomarkers exploitable for risk assessment, early detection and prognosis of MDs. Preanalytical plasma/serum treatment, such as combinatorial peptide ligand libraries with nano-liquid chromatography coupled with tandem MS or selected reaction monitoring coupled to triple-quadrupole time-of-flight instruments, are proven clinical laboratory techniques for quantitative analyses. New strategies, such as SWATH™ MS, which allows us to systematically characterize and quantify query sample sets of 'any protein of interest' in complex biological samples, may dramatically improve next-generation MD biomarkers, especially considering the plethora of candidates coming from the 'bioreactor' gut microbiota affecting MD onset and progression.