Evaluating the diagnostic potential of gelsolin in gestational diabetes mellitus: A case-control study

OBJECTIVES

To investigate the association between gestational diabetes mellitus (GDM) and blood levels of gelsolin (an inflammation-related protein thought to be reduced in type 2 diabetes mellitus) and to determine its role in potential diagnosis and neonatal outcomes.

METHODS

This prospective case-control study was conducted at Ankara Etlik City Hospital between November 2023 and February 2024 with 40 pregnant women with GDM and 40 normoglycemic women. Pregnant women aged 18-40 years who were in their 24th to 28th week of pregnancy and had no known chronic disease were included in the present study and it was investigated as to whether there was a significant difference between the two groups in terms of gelsolin levels and neonatal outcome.

RESULTS

Gelsolin level was statistically significantly lower in the GDM group than in the control group (P = 0.004). In patients with fasting blood glucose <96 mg/dL, maternal serum gelsolin levels were associated with GDM, with a cut-off of 15.38 or less, showing a sensitivity of 73%, a specificity of 67%, and an area under the curve (AUC) of 0.703 (95% confidence interval [CI] 0.576-0.810, P = 0.002). There was no difference between groups in terms of adverse obstetric outcomes, but gelsolin levels were associated with composite neonatal adverse outcome (macrosomia, Apgar score at 5 min less than 7, preterm birth, need for neonatal intensive care), with a cut-off value of 16.66 or less showing a sensitivity of 84.6%, specificity of 40.7% and AUC of 0.644 (95% CI 0.529-0.748, P = 0.031).

CONCLUSION

Gelsolin could potentially serve as a promising biomarker for the diagnosis of GDM.

A systematically investigation of plasma complement and coagulation-related proteins and adiponectin in gestational diabetes mellitus by multiple reaction monitoring technology

BACKGROUND

Gestational diabetes mellitus (GDM) is defined as a glucose intolerance resulting in hyperglycaemia of variable severity with onset during pregnancy, and is prevalent worldwide. The study of diagnostic markers of GDM in early pregnancy is important for early diagnosis and early intervention of GDM. The aim of this study was to search for biomarkers of GDM in early and mid-pregnancy using a targeted proteomics approach.

METHODS

Through multiple response monitoring (MRM) technology and bioinformatics analysis including machine learning, 44 proteins associated with complement and coagulation cascades, and one protein, adiponectin, which is frequently reported to be associated with GDM, were targeted for quantitative analysis, and potential biomarkers were screened.

RESULTS

The results showed that 7 and 6 proteins were identified as differentially expressed proteins (DEPs) between pregnant women subsequently diagnosed with GDM and controls during the first trimester, as well as between GDM cases and controls during the second trimester, respectively. Among them, C1QC and CFHR1 may serve as early predictive markers, and C1QC and adiponectin may serve as mid-term diagnostic markers.

DISCUSSION

Complement and coagulation-related proteins and adiponectin, have been implicated in the pathogenesis of GDM, and some of these proteins have the potential to serve as markers for the prediction or diagnosis of GDM.

iTRAQ proteomics analysis of placental tissue with gestational diabetes mellitus

BACKGROUND

Gestational diabetes mellitus is an endocrine and metabolic disorder that appears for the first time during pregnancy and causes varying degrees of short- and/or long-term effects on the mother and child. The etiology of the disease is currently unknown and isobaric tags for relative and absolute quantitation proteomics approach, the present study attempted to identify potential proteins in placental tissues that may be involved in the pathogenesis of GDM and adverse foetal pregnancy outcomes.

METHODS

Pregnant women with GDM hospitalised were selected as the experimental group, and pregnant women with normal glucose metabolism as the control group. The iTRAQ protein quantification technology was used to screen the differentially expressed proteins between the GDM group and the normal control group, and the differentially expressed proteins were analysed by GO, KEGG, PPI, etc., and the key proteins were subsequently verified by western blot.

RESULTS

Based on the proteomics of iTRAQ, we experimented with three different samples of placental tissues from GDM and normal pregnant women, and the total number of identified proteins were 5906, 5959, and 6017, respectively, which were similar in the three different samples, indicating that the results were reliable. Through the Wayne diagram, we found that the total number of proteins coexisting in the three groups was 4475, and 91 differential proteins that could meet the quantification criteria were strictly screened, of which 32 proteins were up-regulated and 59 proteins were down-regulated. By GO enrichment analysis, these differential proteins are widely distributed in extracellular membrane-bounded organelle, mainly in extracellular exosome, followed by intracellular vesicle, extracellular organelle. It not only undertakes protein binding, protein complex binding, macromolecular complex binding, but also involves molecular biological functions such as neutrophil degranulation, multicellular organismal process, developmental process, cellular component organization, secretion, regulated exocytosis. Through the analysis of the KEGG signaling pathway, it is found that these differential proteins are mainly involved in HIF-1 signaling pathway, Glycolysis/Gluconeogenesis, Central carbon metabolism in cancer, AMPK signaling pathway, Proteoglycans in cancer, Protein processing in endoplasmic reticulum, Thyroid cancer, Alcoholism, Glucagon signaling pathway.

DISCUSSION

This preliminary study helps us to understand the changes in the placental proteome of GDM patients, and provides new insights into the pathophysiology of GDM.

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.

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.

Proteomic analysis of milk fat globule membrane proteins in mature human milk of women with and without gestational diabetes mellitus

Milk fat globule membrane proteins (MFGMP) in human milks have positive effects on infant's health. As gestational diabetes mellitus (GDM) causes variations in MFGMP, it is essential to understand the effects of GDMon MFGMP. This study aims to investigate and compare the MFGMP (>3 months postpartum) of GDM and non-GDM (NGDM) women using four-dimensional-data-independent-acquisition proteomics technology. Principal component analysis shows significant differences in the MFGMP of GDM and NGDM women. A total of 4747 MFGMP were identified in maturehuman milk of GDM and NGDM women. Among these proteins, 174 differentially expressed proteins (DEPs) were identified in MFGM of GDM and NGDM women. Albumin (FC = 7.96) and transthyretin (FC = 2.57) which are related to insulin resistance and involved in thyroid hormone synthesis, are significantly up-regulated in MFGMP of GDM mothers indicating insulin resistance, imbalance of glucose homeostasis and poor glucose metabolism might persist in postpartum period.

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.

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.

Postpartum defects in inflammatory response after gestational diabetes precede progression to type 2 diabetes: a nested case-control study within the SWIFT study

BACKGROUND

Gestational diabetes (GDM) is a distinctive form of diabetes that first presents in pregnancy. While most women return to normoglycemia after delivery, they are nearly ten times more likely to develop type 2 diabetes than women with uncomplicated pregnancies. Current prevention strategies remain limited due to our incomplete understanding of the early underpinnings of progression.

AIM

To comprehensively characterize the postpartum profiles of women shortly after a GDM pregnancy and identify key mechanisms responsible for the progression to overt type 2 diabetes using multi-dimensional approaches.

METHODS

We conducted a nested case-control study of 200 women from the Study of Women, Infant Feeding and Type 2 Diabetes After GDM Pregnancy (SWIFT) to examine biochemical, proteomic, metabolomic, and lipidomic profiles at 6-9 weeks postpartum (baseline) after a GDM pregnancy. At baseline and annually up to two years, SWIFT administered research 2-hour 75-gram oral glucose tolerance tests. Women who developed incident type 2 diabetes within four years of delivery (incident case group, n = 100) were pair-matched by age, race, and pre-pregnancy body mass index to those who remained free of diabetes for at least 8 years (control group, n = 100). Correlation analyses were used to assess and integrate relationships across profiling platforms.

RESULTS

At baseline, all 200 women were free of diabetes. The case group was more likely to present with dysglycemia (e.g., impaired fasting glucose levels, glucose tolerance, or both). We also detected differences between groups across all omic platforms. Notably, protein profiles revealed an underlying inflammatory response with perturbations in protease inhibitors, coagulation components, extracellular matrix components, and lipoproteins, whereas metabolite and lipid profiles implicated disturbances in amino acids and triglycerides at individual and class levels with future progression. We identified significant correlations between profile features and fasting plasma insulin levels, but not with fasting glucose levels. Additionally, specific cross-omic relationships, particularly among proteins and lipids, were accentuated or activated in the case group but not the control group.

CONCLUSIONS

Overall, we applied orthogonal, complementary profiling techniques to uncover an inflammatory response linked to elevated triglyceride levels shortly after a GDM pregnancy, which is more pronounced in women who progress to overt diabetes.

Comparative predictive power of serum vs plasma proteomic signatures in feto-maternal medicine

BACKGROUND

Blood proteins are frequently measured in serum or plasma, because they provide a wealth of information. Differences in the ex vivo processing of serum and plasma raise concerns that proteomic health and disease signatures derived from serum or plasma differ in content and quality. However, little is known about their respective power to predict feto-maternal health outcomes. Predictive power is a sentinel characteristic to determine the clinical use of biosignatures.

OBJECTIVE

This study aimed to compare the power of serum and plasma proteomic signatures to predict a physiological pregnancy outcome.

STUDY DESIGN

Paired serum and plasma samples from 73 women were obtained from biorepositories of a multinational prospective cohort study on pregnancy outcomes. Gestational age at the time of sampling was the predicted outcome, because the proteomic signatures have been validated for such a prediction. Multivariate and cross-validated models were independently derived for serum and plasma proteins.

RESULTS

A total of 1116 proteins were measured in 88 paired samples from 73 women with a highly multiplexed platform using proximity extension technology (Olink Proteomics Inc, Watertown, MA). The plasma proteomic signature showed a higher predictive power (R=0.64; confidence interval, 0.42-0.79; P=3.5×10^-6) than the serum signature (R=0.45; confidence interval, 0.18-0.66; P=2.2×10^-3). The serum signature was validated in plasma with a similar predictive power (R=0.58; confidence interval, 0.34-0.75; P=4.8×10^-5), whereas the plasma signature was validated in serum with reduced predictive power (R=0.53; confidence interval, 0.27-0.72; P=2.6×10^-4). Signature proteins largely overlapped in the serum and plasma, but the strength of association with gestational age was weaker for serum proteins.

CONCLUSION

Findings suggest that serum proteomics are less informative than plasma proteomics. They are compatible with the view that the partial ex-vivo degradation and modification of serum proteins during sample processing are an underlying reason. The rationale for collecting and analyzing serum and plasma samples should be carefully considered when deriving proteomic biosignatures to ascertain that specimens of the highest scientific and clinical yield are processed. Findings suggest that plasma is the preferred matrix.

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.

Human Milk Oligosaccharides Variation in Gestational Diabetes Mellitus Mothers

Gestational diabetes mellitus (GDM) is a common disease of pregnancy, but with very limited knowledge of its impact on human milk oligosaccharides (HMOs) in breast milk. This study aimed to explore the lactational changes in the concentration of HMOs in exclusively breastfeeding GDM mothers and the differences between GDM and healthy mothers. A total of 22 mothers (11 GDM mothers vs. 11 healthy mothers) and their offspring were enrolled in the study and the levels of 14 HMOs were measured in colostrum, transitional milk, and mature milk. Most of the HMOs showed a significant temporal trend with decreasing levels over lactation; however, there were some exceptions for 2′-Fucosyllactose (2′-FL), 3-Fucosyllactose (3-FL), Lacto-N-fucopentaose II (LNFP-II), and Lacto-N-fucopentaose III (LNFP-III). Lacto-N-neotetraose (LNnT) was significantly higher in GDM mothers in all time points and its concentrations in colostrum and transitional milk were correlated positively with the infant’s weight-for-age Z-score at six months postnatal in the GDM group. Significant group differences were also found in LNFP-II, 3′-Sialyllactose (3′-SL), and Disialyllacto-N-tetraose (DSLNT) but not in all lactational periods. The role of differently expressed HMOs in GDM needs to be further explored by follow-up studies.

Molecular Pathways of Diabetic Kidney Disease Inferred from Proteomics

Diabetic kidney disease (DKD) affects an estimated 20-40% of type 2 diabetes patients and is among the most prevalent microvascular complications in this patient population, contributing to high morbidity and mortality rates. Currently, changes in albuminuria status are thought to be a primary indicator of the onset or progression of DKD, yet progressive nephropathy and renal impairment can occur in certain diabetic individuals who exhibit normal urinary albumin levels, emphasizing the lack of sensitivity and specificity associated with the use of albuminuria as a biomarker for detecting diabetic kidney disease and predicting DKD risk. According to the study, a non-invasive method for early detection or prediction of DKD may involve combining proteomic analytical techniques such second generation sequencing, mass spectrometry, two-dimensional gel electrophoresis, and other advanced system biology algorithms. Another category of proteins of relevance may now be provided by renal tissue biomarkers. The establishment of reliable proteomic biomarkers of DKD represents a novel approach to improving the diagnosis, prognostic evaluation, and treatment of affected patients. In the present review, a series of protein biomarkers that have been characterized to date are discussed, offering a theoretical foundation for future efforts to aid patients suffering from this debilitating microvascular complication.

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.

Potential biomarkers identified in plasma of patients with gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is a common complication during pregnancy. Looking for reliable diagnostic markers for early diagnosis can reduce the impact of the disease on the fetus OBJECTIVE: The present study is designed to find plasma metabolites that can be used as potential biomarkers for GDM, and to clarify GDM-related mechanisms METHODS: By non-target metabolomics analysis, compared with their respective controls, the plasma metabolites of GDM pregnant women at 12-16 weeks and 24-28 weeks of pregnancy were analyzed. Multiple reaction monitoring (MRM) analysis was performed to verify the potential marker RESULTS: One hundred and seventy-two (172) and 478 metabolites were identified as differential metabolites in the plasma of GDM pregnant women at 12-16 weeks and 24-28 weeks of pregnancy, respectively. Among these, 40 metabolites were overlapped. Most of them are associated with the mechanism of diabetes, and related to short-term and long-term complications in the perinatal period. Among them, 7 and 10 differential metabolites may serve as potential biomarkers at the 12-16 weeks and 24-28 weeks of pregnancy, respectively. By MRM analysis, compared with controls, increased levels of 17(S)-HDoHE and sebacic acid may serve as early prediction biomarkers of GDM. At 24-28 weeks of pregnancy, elevated levels of 17(S)-HDoHE and L-Serine may be used as auxiliary diagnostic markers for GDM CONCLUSION: Abnormal amino acid metabolism and lipid metabolism in patients with GDM may be related to GDM pathogenesis. Several differential metabolites identified in this study may serve as potential biomarkers for GDM prediction and diagnosis.

Proteomics Analysis of Serum Proteins in Gestational Diabetes

OBJECTIVE

The purpose of this study was to screen serum proteins for biomarkers of gestational diabetes mellitus (GDM) and to investigate its pathogenesis by analyzing the differences in serum proteomics between pregnant women with GDM and healthy pregnant women.

METHODS

Patients who were admitted to the First Affiliated Hospital of Fujian Medical University from June 2019 to January 2020 were included. According to the medical history and the results of the 75 g oral glucose tolerance test (OGTT), they were divided into the normal pregnant women group and GDM pregnant women group. The serum of two groups of patients was collected. High performance liquid chromatography-mass spectrometry was used to identify differentially expressed serum proteins between pregnant women with GDM and healthy pregnant women, and bioinformatics analysis was then performed on the identified proteins.

RESULTS

A total of 1152 quantifiable proteins were detected; among them, 15 were upregulated in serum of GDM pregnant women, while 26 were downregulated. The subsequent parallel reaction monitoring (PRM) assay validated the expression levels of 12 out of 41 differentially expressed proteins. Moreover, bioinformatics analysis revealed that the differentially expressed proteins are involved in multiple biological processes and signaling pathways related to the lipid metabolism, glycan degradation, immune response, and platelet aggregation.

CONCLUSIONS

This study identified 41 serum proteins with differential expression between pregnant women with GDM and healthy pregnant women, providing new candidate molecules for elucidating GDM pathogenesis and screening therapeutic targets.

Alterations of transcriptome expression, cell cycle, and mitochondrial superoxide reveal foetal endothelial dysfunction in Saudi women with gestational diabetes mellitus

Gestational diabetes mellitus (GDM) affects one in four Saudi women and is associated with high risks of cardiovascular diseases in both the mother and foetus. It is believed that endothelial cells (ECs) dysfunction initiates these diabetic complications. In this study, differences in the transcriptome profiles, cell cycle distribution, and mitochondrial superoxide (MTS) between human umbilical vein endothelial cells (HUVECs) from GDM patients and those from healthy (control) subjects were analysed. Transcriptome profiles were generated using high-density expression microarray. The selected four altered genes were validated using qRT-PCR. MTS and cell cycle were analysed by flow cytometry. A total of 84 altered genes were identified, comprising 52 upregulated and 32 downregulated genes in GDM.HUVECs. Our selection of the four interested altered genes (TGFB2, KITLG, NEK7, and IGFBP5) was based on the functional network analysis, which revealed that these altered genes are belonging to the highest enrichment score associated with cellular function and proliferation; all of which may contribute to ECs dysfunction. The cell cycle revealed an increased percentage of cells in the G2/M phase in GDM.HUVECs, indicating cell cycle arrest. In addition, we found that GDM.HUVECs had increased MTS generation. In conclusion, GDM induces persistent impairment of the biological functions of foetal ECs, as evidenced by analyses of transcriptome profiles, cell cycle, and MTS even after ECs culture in vitro for several passages under normal glucose conditions.

Gestational Diabetes Mellitus-Induced Changes in Proteomes and Glycated/Glycosylated Proteomes of Human Colostrum

Gestational diabetes mellitus (GDM) not only has a bad effect on the development of infants but also causes variations in breastmilk composition. This study aims to investigate the changes in the protein profile of colostrum between mothers with GDM and healthy mothers (H) by sequential windowed acquisition of all theoretical fragment ion proteomics techniques. A total of 1295 proteins were detected, with 192 proteins being significantly different between GDM and H. These significantly different proteins were enriched with the carbohydrate and lipid metabolism pathway as well as immunity. Some proteins had an AOC value of 1, such as apolipoprotein E and lipoprotein lipase. In addition, we identified 42 glycated and 93 glycosylated peptides in colostrum without any enrichment, with glycated peptides being upregulated and glycosylated peptides being downregulated in colostrum with GDM. These results help us to better understand the GDM-induced changes in proteomes and glycated and glycosylated level and provide guidance on infant formula adjustment for infants from mothers with GDM.

The role of complement factor H in gestational diabetes mellitus and pregnancy

BACKGROUND

Complement factor H (CFH) has been found to be associated with insulin resistance. This study assessed the correlation between CFH and other clinical parameters, and determined whether CFH played a role in gestational diabetes mellitus (GDM) and adverse pregnancy outcomes.

METHODS

A total of 397 pregnant women were included for analysis in this nested case-control study. Clinical parameters and serum were collected within the 11-17th gestational age at the first prenatal visit. At 24-28 weeks of gestation, a 75 g oral glucose tolerance test was performed and subjects were divided into a GDM (n = 80) and a non-GDM control group (n = 317). The delivery data were also followed. The serum CFH level was assayed by ELISA.

RESULTS

CFH was higher in GDM than in non-GDM controls (280.02 [58.60] vs. 264.20 [68.77]; P = 0.014). CFH level was moderately associated with pre-pregnancy body mass index (BMI), BMI and total triglycerides (TG), and slightly associated with gestational age, low density lipoprotein cholesterol (LDL-C), total cholesterol (TC) in GDM and non-GDM (all P <  0.05). Moreover, CFH level was moderately correlated with alkaline phosphatase (ALP) and slightly correlated with age, uric acid (UA) and total bilirubin (TB) in non-GDM (all P <  0.05). After adjustment for clinical confounding factors, BMI, TG, gestational age, ALP, TB, age and UA were independent risk factors for log₁₀ CFH levels (all P <  0.05) in all subjects. In addition, overweight or obese pregnant women, women with hypertriglyceridemia and women in the second trimester had significantly higher CFH levels than normal weight and underweight group (P <  0.001), the non-hypertriglyceridemia group (P <  0.001) and women in the first trimester group (P < 0.05) in all pregnant women respectively. Following binary logistic regression, CFH was not independently associated with GDM and related pregnant outcomes.

CONCLUSIONS

The CFH in 11-17th weeks of gestation might be affected by many factors, including BMI, TG, gestational age, ALP, TB, age and UA. CFH was not an independent risk factor for GDM and avderse pregnancy outcomes.

Potential biomarkers for inherited thrombocytopenia 2 identified by plasma proteomics

Inherited thrombocytopenia 2 (THC2) is difficult to diagnose due to the lack of specific clinical characteristics and diagnostic methods. To identify potential plasma protein biomarkers for THC2, we collected the plasma samples from a THC2 family (9 THC2 and 15 non-THC2 members), enriched the medium and low abundant proteins using Proteominer and analyzed the protein profiles using the liquid chromatography-mass spectrometry in data independent acquisition mode. Initially, we detected 784 proteins in the plasma samples of this family and identified 27 up-regulated and 36 down-regulated in the THC2 group compared to the non-THC2 group (|log₂ ratio| >1 and p-value <0.05). To improve the predictive power, top eight dysregulated proteins (B7Z2B4, LTF, HP, ERN1, IGHV1-8, A0A0X9V9C4, VH6DJ, and D3JV41) were selected by an area under the curve-based random forest process to construct a clinical model. Multivariate analysis with random forest and support vector machine showed that the prediction model provided high discrimination ability for THC2 diagnosis (AUC: 1.000 and 0.967, respectively). The potential plasma protein biomarkers will be tested in more THC2 patients and other thrombocytopenia patients to further validate their specificity and sensitivity.

Use of data-independent acquisition mass spectrometry for comparative proteomics analyses of sera from pregnant women with intrahepatic cholestasis of pregnancy

We used data-independent acquisition (DIA) proteomics technology followed by ELISAs and automated biochemical analyses to identify and validate protein expression levels in Intrahepatic Cholestasis of Pregnancy (ICP) and healthy pregnant controls. We employed bioinformatics to identify metabolic processes associated with differentially expressed proteins.The expression levels of two proteins (S100-A9 and the L-lactate dehydrogenase A chain) were significantly higher in ICP patients than in controls; the areas under the receiver operating characteristic (ROC) curves (AUCs) were 0.774 and 0.828, respectively. The expression levels of two other proteins (apolipoprotein A-I and cholinesterase) were significantly lower in patients, with values of 0.900 and 0.842, respectively. Multiple logistic regression showed that a combination of the levels of the four proteins optimized the AUC (0.962), thus more reliably diagnosing ICP. The levels of all four proteins were positively associated with that of total bile acids. Bioinformatics analyses indicated that the four proteins principally affected neutrophil activation involved in the immune response, cell adhesion, lipoprotein metabolism, and the PPAR signaling pathway. SIGNIFICANCE: This preliminary work improves our understanding of changes in serum levels of protein in pregnant women with ICP. The four proteins may serve as novel noninvasive biomarkers for ICP.

Quantitative Comparative Proteomics Reveals Candidate Biomarkers for the Early Prediction of Gestational Diabetes Mellitus: A Preliminary Study

AIM

To identify differentially expressed proteins (DEPs) in 1^st trimester maternal plasma between pregnant women at risk for gestational diabetes mellitus (GDM) and uncomplicated controls.

MATERIALS AND METHODS

First-trimester plasma from five women who developed GDM and five from non-diabetic ones were analyzed using isobaric tag for relative and absolute quantitation - labeled proteomics. Enzyme-linked immunosorbent assay was further applied in an independent cohort of 25 GDM cases and 25 controls for verification.

RESULTS

Prenylcysteine oxidase 1 (PCYOX1), beta-ala-his dipeptidase (CNDP1), extracellular matrix protein 1 (ECM1), basement membrane-specific heparan sulfate proteoglycan core protein (HSPG2), thrombospondin-4 (TSP-4) demonstrated significant differences in expression between the two groups (p<0.05). DEPs are mainly associated with complement and coagulation cascades.

CONCLUSION

The reported plasma proteomic changes represent potential biomarkers for the early identification of women at risk for GDM. Future studies using larger and more diverse cohorts are necessary to assess the clinical utility of these findings.

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.

Screening and analysis of small molecular peptides in urine of gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is a common clinical disease with complicated clinical process and harmful effects on pregnant women and fetus. The purpose of this study is to use the convenient urine samples in combination with glucose levels to detect or predict GDM. In this study, urine samples of non-pregnant women, normal pregnant women and GDM patients were collected. The peptides in urine were enriched by weak cationic exchange magnetic beads (MB-WCX) and analyzed by matrix assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF MS). 46 polypeptide peaks with statistical difference (P < 0.01) were screened out by using Bioexplorer analysis software. The level of molecules with mass-to-charge ratio of 1079.2, 1290.6 and 1500.7 was higher in the GDM group than the other two groups. Through the analysis of differential molecules by liquid chromatography tandem mass spectrometry (LC-MS), the above molecules were identified as coagulation factor IX, TBC1 family member 5 isoform a [Homo sapiens] (TBC1D5a) and immunoglobulin kappa constant. The discovery of polypeptides provides the research basis for further primary screening and assistant diagnosis of GDM through urine samples.