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

Proteomics for early prenatal screening of gestational diabetes mellitus

In this editorial, we comment on the article by Cao et al. Through applying isobaric tags for relative and absolute quantification technology coupled with liquid chromatography-tandem mass spectrometry, the researchers observed significant differential expression of 47 proteins when comparing serum samples from pregnant women with gestational diabetes mellitus (GDM) to the healthy ones. GDM symptoms may involve abnormalities in inflammatory response, complement system, coagulation cascade activation, and lipid metabolism. Retinol binding protein 4 and angiopoietin like 8 are potential early indicators of GDM. GDM stands out as one of the most prevalent metabolic complications during pregnancy and is linked to severe maternal and fetal outcomes like pre-eclampsia and stillbirth. Nevertheless, none of the biomarkers discovered so far have demonstrated effectiveness in predicting GDM. Our topic was designed to foster insights into advances in the application of proteomics for early prenatal screening of GDM.

Simple and Fast Prediction of Gestational Diabetes Mellitus Based on Machine Learning and Near-Infrared Spectra of Serum: A Proof of Concept Study at Different Stages of Pregnancy

Gestational diabetes mellitus (GDM) is a hyperglycemic state that is typically diagnosed by an oral glucose tolerance test (OGTT), which is unpleasant, time-consuming, has low reproducibility, and results are tardy. The machine learning (ML) predictive models that have been proposed to improve GDM diagnosis are usually based on instrumental methods that take hours to produce a result. Near-infrared (NIR) spectroscopy is a simple, fast, and low-cost analytical technique that has never been assessed for the prediction of GDM. This study aims to develop ML predictive models for GDM based on NIR spectroscopy, and to evaluate their potential as early detection or alternative screening tools according to their predictive power and duration of analysis. Serum samples from the first trimester (before GDM diagnosis) and the second trimester (at the time of GDM diagnosis) of pregnancy were analyzed by NIR spectroscopy. Four spectral ranges were considered, and 80 mathematical pretreatments were tested for each. NIR data-based models were built with single- and multi-block ML techniques. Every model was subjected to double cross-validation. The best models for first and second trimester achieved areas under the receiver operating characteristic curve of 0.5768 ± 0.0635 and 0.8836 ± 0.0259, respectively. This is the first study reporting NIR-spectroscopy-based methods for the prediction of GDM. The developed methods allow for prediction of GDM from 10 µL of serum in only 32 min. They are simple, fast, and have a great potential for application in clinical practice, especially as alternative screening tools to the OGTT for GDM diagnosis.

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.

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.

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.

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.

Analysis of Spatiotemporal Urine Protein Dynamics to Identify New Biomarkers for Sepsis-Induced Acute Kidney Injury

Acute kidney injury (AKI) is a frequent complication of sepsis and contributes to increased mortality. Discovery of reliable biomarkers could enable identification of individuals with high AKI risk as well as early AKI detection and AKI progression monitoring. However, the current methods are insensitive and non-specific. This study aimed to identify new biomarkers through label-free mass spectrometry (MS) analysis of a sepsis model induced by cecal ligation and puncture (CLP). Urine samples were collected from septic rats at 0, 3, 6, 12, 24, and 48 h. Protein isolated from urine was subjected to MS. Immunoregulatory biological processes, including immunoglobin production and wounding and defense responses, were upregulated at early time points. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses identified 77 significantly changed pathways. We further examined the consistently differentially expressed proteins to seek biomarkers that can be used for early diagnosis. Notably, the expression of PARK7 and CDH16 were changed in a continuous manner and related to the level of Scr in urine from patients. Therefore, PARK7 and CDH16 were confirmed to be novel biomarkers after validation in sepsis human patients. In summary, our study analyzed the proteomics of AKI at multiple time points, elucidated the related biological processes, and identified novel biomarkers for early diagnosis of sepsis-induced AKI, and our findings provide a theoretical basis for further research on the molecular mechanisms.

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.

Omics-based biomarkers in the diagnosis of diabetes

Diabetes mellitus (DM) is a group of metabolic diseases related to the dysfunction of insulin, causing hyperglycaemia and life-threatening complications. Current early screening and diagnostic tests for DM are based on changes in glucose levels and autoantibody detection. This review evaluates recent studies on biomarker candidates in diagnosing type 1, type 2 and gestational DM based on omics classification, whilst highlighting the relationship of these biomarkers with the development of diabetes, diagnostic accuracy, challenges and future prospects. In addition, it also focuses on possible non-invasive biomarker candidates besides common blood biomarkers.

Gestational Diabetes Mellitus Regulatory Network Identifies hsa-miR-145-5p and hsa-miR-875-5p as Potential Biomarkers

BACKGROUND

Gestational diabetes mellitus (GDM) is pregnancy-related diabetes with vital risks for both mother and the fetus. Molecular studies represent one of the popular approaches for investigating mechanisms associated with the disease nature. One of which is through interaction network analysis via Cytoscape V. 3.6.1.

METHODS

In this study, the microRNA (miRNA) expression array of GSE98043 from gene expression omnibus (GEO) database was retrieved and screened. We identified 12 differentially expressed (DE) miRNAs (P ≤ 0.05) and nine target hub-bottleneck genes (disease score > 1) for GDM based on miRNA-target interactions created via plugin ClueGO + Cluepedia + STRING.

RESULTS

MiRNA-target information showed that the miRNAs are mostly up-regulated and hsa-miR-145-5p and hsa-miR-875-5p targets the most genes. Among target genes, IL6, GCG, APOB, and ALB have the highest associations with DE-miRNAs. Gene ontology analysis based on biological processes identification via ClueGO + CluePedia, in addition, showed that target hub-bottlenecks are mainly related to metabolism functions and any changes in this regulatory network could impose fundamental alterations in these processes.

CONCLUSIONS

It can be concluded that via these introduced miRNAs and their targets, the molecular tests for diagnosis and treatment of GDM can be improved after applying validation approaches.

Controversies in Screening and Diagnostic Criteria for Gestational Diabetes in Early and Late Pregnancy

This review serves to evaluate the screening and diagnostic strategies for gestational diabetes and overt diabetes in pregnancy. We focus on the different early screening and diagnostic approaches in first trimester including fasting plasma glucose, random plasma glucose, oral glucose tolerance test, hemoglobin A1c, risk prediction models and biomarkers. Early screening for gestational diabetes is currently not recommended since the potential benefits and harms of early detection and subsequent treatment need to be further evaluated in randomized controlled trials.