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

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.

Clinical decision-making in bone cancer care management and forecast of ICU needs based on computed tomography

Objective

This study aimed to evaluate the role of computed tomography (CT) imaging in the diagnosis and management of bone cancer during periods of limited access to histopathological testing. We aimed to determine the correlation between CT severity levels and subsequent patient management and care decisions, adhering to established oncological CT reporting guidelines.

Methodology

A retrospective analysis was conducted on 60 symptomatic patients from January 2021 to January 2024. The cohort included patients aged between 50 and 86 years, with a mean age of 68 years, and 75 % were male. All patients had their bone cancer diagnosis confirmed through histopathological examination, and CT imaging was used as the reference method. The analysis involved assessing the correlation between CT severity scores and patient management, including ICU admissions.

Results

The study found that CT imaging demonstrated a sensitivity of 92.6% in diagnosing bone cancer, with accuracy increasing to 97.6% in cases with high-probability CT characteristics. CT specificity also showed a consistent rise. Osteolytic lesions were the predominant finding, detected in 85.9% of cases. Among these, 88% exhibited engagement across multiple skeletal regions, 92.8% showed bilateral distribution, and 92.8% presented with peripheral involvement. In ICU patients, bone consolidation was observed in 81.5% of cases and was predominant in 66.7% of the ICU cohort. Additionally, ICU patients had significantly higher CT severity scores, with scores exceeding 14 being notably prevalent.

Conclusions

During the management period of bone cancer at our hospital, characteristic features on CT imaging facilitated swift and sensitive investigation. Two distinct CT phenotypes, associated with the primary osteolytic phenotype and severity score, emerged as valuable indicators for assessing the severity of the disease, particularly during ICU care. These findings highlight the diverse manifestations and severity levels encountered in bone cancer patients and underscore the importance of CT imaging in their diagnosis and management.

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.

Integrative metabolome and lipidome analyses of plasma in neovascular macular degeneration

Age-related macular degeneration (AMD) causes irreversible vision-loss among the elderly in industrial countries. Neovascular AMD (nAMD), which refers to late-stage AMD, is characterized by severe vision-threatening choroidal neovascularization (CNV). Herein, we constructed a global metabolic network of nAMD, based on untargeted metabolomic and lipidomic analysis of plasma samples collected from sixty subjects (30 nAMD patients and 30 age-matched controls). Among the nAMD and control groups, 62 and 44 significantly different metabolites were detected in the positive and negative ion modes, respectively. Grouping analysis further showed that lipid and lipid-like molecule-based superclasses contained the highest number of significantly different metabolites. Lipidomic analysis revealed that 53 lipids among the nAMD and control groups differed significantly; these belonged to four major lipid categories (glycerophospholipids, sphingolipids, glycerolipids, and fatty acids). A discriminative biomarker panel comprising 16 metabolites and lipids, which was constructed using multivariate statistical machine learning methods, could effectively identify nAMD cases. Among these 16 compounds, eight were lipids that belonged to three lipid categories (glycerophospholipids, sphingolipids, and prenol lipids). The top three biomarkers with the highest importance scores were all lipids (a glycerophospholipid and two sphingolipids), highlighting the crucial role played by glycerophospholipid and sphingolipid pathways in nAMD. These differences between the metabolic and lipid profiles of nAMD patients and elderly individuals without AMD provide a readout of the overall metabolic status of nAMD. Further insights into the identified discriminative biomarkers may pave the way for future diagnostic and therapeutic interventions for nAMD.

PDMS-TiO2 composite films combined with LC-MS/MS for determination of phospholipids of urine in non-small cell lung cancer patients with traditional Chinese medicine syndromes

Lung cancer is one of the most common malignant tumors in China. Most patients are already in the mid to advanced stages during the consultation and the survival rate is less than 23 % with a poor prognosis. Therefore, effective dialectical diagnosis of advanced cancer can guide individualized treatment to improve survival. Phospholipids are the building blocks of cell membranes and abnormal phospholipid metabolism is associated with plentiful diseases. Most studies of disease markers use blood as a sample. However, urine covers extensive metabolites that are produced during the body's metabolic processes. Therefore, the study of markers in urine can be used as a complement to improve the diagnosis rate of marker diseases. Moreover, urine is characterized by high water content, high polarity, and high inorganic salt, therefore the detection of phospholipids in urine is challenging. In this study, an original Polydimethylsiloxane (PDMS)-titanium dioxide (TiO₂) composite film for sample pre-treatment coupled with the LC-MS/MS method for the determination of phospholipids in the urine with high selectivity and low matrix effects was prepared and developed. The extraction process was scientifically optimized by the single-factor test. After systematic validation, the established method was successfully applied to the accurate determination of phospholipid substances in the urine of lung cancer patients and healthy subjects. In conclusion, the developed method has great potential for the development of lipid enrichment analysis in urine and can be used as a beneficial tool for cancer diagnosis and Chinese medicine syndrome typing.

Levels of inter-alpha-trypsin inhibitor heavy chain H4 urinary polypeptide in gestational diabetes mellitus

Gestational diabetes mellitus (GDM) can cause a variety of adverse maternal and fetal complications. The purpose of this study was to screen and identify the urinary polypeptides related to the severity of GDM and to analyze the correlation between urinary peptide levels and neonatal metabolic indices. A total of 31 normal pregnant women (N group) and 74 patients with GDM (GDM group) were randomly selected between February 2018 and August 2019. Patients with GDM were divided into two groups according to their fasting plasma glucose (FPG) levels. The urine samples were enriched using weak cation-exchange magnetic beads (MB-WCX), and eight different urine polypeptides were screened and analyzed. The peptide spectra were obtained using matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS). The urinary peptide signatures of the two groups were compared using the BioExplorer software. The difference analysis of the eight urinary polypeptides between the normal pregnant (N) group and GDM group showed that two polypeptides with mass-to- charge ratios (m/z) of 2175.7 and 2318.8, respectively, were significantly different between the two groups (P < 0.01). The m/z 2175.7 polypeptide was analyzed by liquid chromatography-tandem mass spectrometry (LC-MS), and the corresponding name of the molecule was inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4). The changes in ITIH4 levels correlated with those in the neonatal metabolic indices. By establishing the Fisher discriminant function equation for the GDM group, the difference in sample distribution and mean value of the two groups could be observed directly.Abbreviations: GDM: gestational diabetes mellitus; FPG: fasting plasma glucose; MB-WCX: weak cation exchange magnetic beads; MALDI-TOF MS: matrix-assisted laser desorption ionization time-of-flight mass spectrometry; m/z: mass charge ratio; LC-MS: liquid chromatography-tandem mass spectrometry; glycosylated hemoglobin (HbA1c); PPG: postprandial plasma glucose; ITIH4: inter-alpha-trypsin inhibitor heavy chain H4; IR: insulin resistance; NFPG: neonatal fasting plasma glucose; NH: neonatal height; NW: neonatal weight; BMI: body mass index; RPL: recurrent pregnancy loss; OGTT: oral glucose tolerance test; ADA: American Diabetes Association; LIS: Laboratory Information System.

Establishment of clinical diagnostic models using glucose, lipid, and urinary polypeptides in gestational diabetes mellitus

BACKGROUND

Gestational diabetes mellitus (GDM) has many adverse outcomes that seriously threaten the short-term and long-term health of mothers and infants. This study comprehensively analyzed the clinical diagnostic value of GDM-related clinical indexes and urine polypeptide research results, and established comprehensive index diagnostic models.

METHODS

In this study, diagnostic values from the clinical indexes of serum triglyceride (TRIG), high-density lipoprotein cholesterol (HDL-C), fasting plasma glucose (FPG) and glycosylated hemoglobin (HbA1c), and 7 GDM-related urinary polypeptides were analyzed retrospectively. The multiple logistic regression equation, multilayer perceptron neural network model, radial basis function, and discriminant analysis function models of GDM-related indexes were established using machine language.

RESULTS

The results showed that HbA1c had the highest diagnostic value for GDM, with an area under the curve (AUC) of 0.769. When the cut-off value was 4.95, the diagnostic sensitivity and specificity were 70.5% and 70.0%, respectively. Among the seven GDM-related urinary polypeptides, human hemopexin (HEMO) had the highest diagnostic value, with an AUC of 0.690. When the cut-off value was 368.5, the sensitivity and specificity were 79.5% and 43.3%, respectively. The AUC of the multilayer perceptron neural network model was 0.942, followed by binary logistic regression (0.938), radial basis function model (0.909), and the discriminant analysis function model (0.908).

CONCLUSION

The establishment of a GDM diagnostic model combining blood glucose, blood lipid, and urine polypeptide indexes can lay a foundation for exploring machine language and artificial intelligence in diagnostic systems.

Urinary Peptides Associated Closely with Gestational Diabetes Mellitus

Gestational diabetes mellitus (GDM) is a common disease of pregnant women, which has a higher incidence in recent years. The purpose of this study is to explore urinary biomarkers that could predict and monitor gestational diabetes mellitus (GDM). Urine samples from 30 normal pregnant women and 78 GDM patients were collected and purified by weak cationic exchange magnetic beads (MB-WCX), then analyzed by matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF-MS). The urinary peptide signatures of the two groups were compared by BioExplorer software. The potential ability of the differently expressed peptides to distinguish GDM patients from normal pregnant women was evaluated by receiver operating characteristic (ROC) analysis. At last, the differently expressed peptides were identified by liquid chromatography tandem mass spectrometry (LC-MS). There were four differently expressed peptides (m/z 1000.5, 1117.5, 1142.9, and 2022.9) between two groups, which were identified as fragments of urinary albumin, α2-macroglobulin, human hemopexin, and α1-microglobulin, respectively. The diagnostic efficacy of m/z 1142.9 was better than the other peptides. The area under the curve (AUC) of the m/z 1142.9 was 0.690 (95% CI: 0.583-0.796). The discovery of urinary polypeptides provides the possibility for the early prediction of GDM and the monitoring of glucose metabolism in GDM patients by a noninvasive method.