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

Hu, Zhiying; Hou, Jun; Zhang, Man

doi:10.1080/19396368.2021.1977869

Cited by 2 publications

(3 citation statements)

References 25 publications

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“… 112 , 113 , 114 , 115 , 116 , 117 , 118 Hu et al. identified two urine peptides that exhibited noteworthy differences between the healthy pregnancy group and GDM group through utilization of matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) MS. 29 One of the two urine peptides was ITIH4, which was detected at higher levels in GDM patients. Tracking of the dynamic changes in urine ITIH4 levels demonstrated no significant difference between the second and third trimesters, providing a basis to study the alterations in secretion patterns of urine peptides during GDM.…”

Section: Urine Proteomicsmentioning

confidence: 97%

“… 28 China ELISA cross-sectional study; GDM (n = 101) vs normal pregnant women (n = 20) vs nonpregnant women (n = 24) N/A ↑ : L-FABP (GDM > normal pregnant women > nonpregnant women) Zhiying Hu et al. 29 China MALDI-TOF/MS case-control study; GDM (n = 74) vs NGT (n = 31) second and third trimesters ↑ : ITIH4 Zhiying Hu et al. 30 China MALDI-TOF/MS case-control study; GDM (n = 60) vs normal pregnant women (n = 31) vs nonpregnant women (n = 31) second and third trimesters ↑ : coagulation factor IX, TBC1D5a and immunoglobulin kappa constant (GDM > normal pregnant women and nonpregnant women) Brian J. KOOS et al.…”

Section: Introductionmentioning

confidence: 99%

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Using metabolomics and proteomics to identify the potential urine biomarkers for prediction and diagnosis of gestational diabetes

Yu,

Ren,

Ren

et al. 2024

eBioMedicine

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Section: Urine Proteomicsmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Using metabolomics and proteomics to identify the potential urine biomarkers for prediction and diagnosis of gestational diabetes

Yu,

Ren,

Ren

et al. 2024

eBioMedicine

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“…MALDI-TOF MS technology combined with magnetic beads has been widely used in the study of a variety of diseases and has led to the discovery of a variety of protein biomarkers, including for cancer ( 12 ), immune diseases ( 13 ), and endocrine diseases ( 14 ). MALDI-TOF MS technology combined with magnetic beads can directly detect all kinds of complex biological samples (e.g., urine, blood, cerebrospinal fluid, tissue biopsies, and even tear samples).…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a decision tree classification model for the essential hypertension based on serum protein biomarkers

Han¹,

Luo²

2022

Ann Transl Med

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Background: Essential hypertension (EH) is a key risk factor for cardiovascular disease. However, the etiology of EH is complex and unknown. So far, there is no good protein biomarker for screening EH. The purpose of this study was to discover potential biomarkers for EH by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and establish a decisiontree classification model. Methods: A total of 108 patients with clinically confirmed EH and 105 HC were enrolled in the present study from September 2020 to April 2021 and were randomly divided into the training group and the blindtest group. The serum protein expression profiles were performed using MALDI-TOF MS combined with magnetic beads with weak cation exchange (MB-WCX). The training group, which comprised 54 EH patients and 53 HC, was used to screen the statistically differential protein peaks by SPSS 19.0 and construct a decision-tree classification model by C5.0 algorithms of SPSS Modeler 18.0. All protein peak intensities of samples in the blind-test group, which comprised 54 EH patients and 52 HC, were used to verify the diagnostic capabilities of the model by classification model.Results: EH patients had higher age, systolic and diastolic blood pressures than HC group. The intensities of 60 protein peaks differed significantly between the EH patients and HC. An optimal decision-tree classification model of EH was successfully established with mass-to-charge ratios of 1,326.7, 1,785.3, 4,228.0, and 8,963.8 as differential protein peaks by the software analysis. The decision-tree classification model was able to distinguish between EH patients and HC and had a sensitivity of 94.44%, a specificity of 94.33%, an accuracy of 94.39%, and an area under the receiver operating characteristic (ROC) curve of 0.96. The blindtest results indicated a sensitivity of 87.04%, a specificity of 88.46%, an accuracy of 87.74%, and an area under the ROC curve of 0.928.Conclusions: MALDI-TOF MS combined with MB-WCX can be used to screen for serum differential protein expression profiles in EH patients. The decision-tree classification model based on mass-to-charge ratios of 1,326.7, 1,785.3, 4,228.0, and 8,963.8 could provide a new and reliable method for screening and identifying EH with high sensitivity and specificity.

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

Cited by 2 publications

References 25 publications

Using metabolomics and proteomics to identify the potential urine biomarkers for prediction and diagnosis of gestational diabetes

Using metabolomics and proteomics to identify the potential urine biomarkers for prediction and diagnosis of gestational diabetes

Development and validation of a decision tree classification model for the essential hypertension based on serum protein biomarkers

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