Editorial Open Access
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. Sep 16, 2024; 12(26): 5850-5853
Published online Sep 16, 2024. doi: 10.12998/wjcc.v12.i26.5850
Proteomics for early prenatal screening of gestational diabetes mellitus
Liang Wu, Xiu-Ping Wang, Yun-Xia Zhu, Chun-Ming Li, Department of Dermatology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330006, Jiangxi Province, China
Yan-Ping Tan, Department of Dermatology, Jiangxi Provincial Maternal and Child Health Hospital, Nanchang 330000, Jiangxi Province, China
ORCID number: Liang Wu (0009-0004-6926-3236); Xiu-Ping Wang (0009-0003-0348-8427); Yun-Xia Zhu (0000-0001-5102-3817); Yan-Ping Tan (0009-0002-1210-7086); Chun-Ming Li (0009-0000-1998-5311).
Co-corresponding authors: Yan-Ping Tan and Chun-Ming Li.
Author contributions: Wu L contributed to data collection and manuscript writing; Zhu YX and Wang XP contributed to data analysis; Tan YP contributed to conceptualization and supervision; Li CM contributed to manuscript polishing and editing; all authors have read and approved the final manuscript. Both Tan YP and Li CM have played important and indispensable roles in the writing and editing of the manuscript and review of the literature as the co-corresponding authors. Tan YP conceptualized and supervised the whole process of the project. She searched the literature, and revised and submitted the early version of the manuscript. Li CM was instrumental and responsible for polishing, editing, and submission of the current version of the manuscript.
Conflict-of-interest statement: All the authors declare that they have no conflict of interest to disclose.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Chun-Ming Li, PhD, Associate Chief Physician, Department of Dermatology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, No. 1 Minde Road, Nanchang 330006, Jiangxi Province, China. chunminglincu@163.com
Received: March 20, 2024
Revised: May 12, 2024
Accepted: June 4, 2024
Published online: September 16, 2024
Processing time: 122 Days and 0.4 Hours

Abstract

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.

Key Words: Gestational diabetes mellitus, Proteomics, Biomarker, Blood, Placenta

Core Tip: In this editorial, we comment on the article by Cao et al. Our topic was designed to foster insights into advances in the application of proteomics for early prenatal screening of gestational diabetes mellitus.



INTRODUCTION

In this editorial, we comment on the article by Cao et al[1]. By applying isobaric tags for relative and absolute quantification (iTRAQ) 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. Furthermore, females diagnosed with GDM have an approximately ten-fold greater risk of developing diabetes later in life. Currently, the guidelines set by professional organizations suggest that all pregnant women should undergo universal screening for GDM using the 75 g oral glucose tolerance test between the 24th and 28th weeks of gestation. Selective screening for GDM during early pregnancy is suggested when certain maternal risk factors (higher maternal age, overweight/obesity, ethnicity, personal history of GDM, family history of diabetes, and previous macrosomia) are present. However, diagnosis of GDM is frequently delayed and confirmed after complications arise[2]. Hence, it is extremely crucial to explore potential biomarkers for GDM prediction during pregnancy to accurately identify pregnant women at high risk of GDM and to develop effective interventions. Nevertheless, none of the biomarkers discovered so far have demonstrated effectiveness in predicting GDM.

Mass spectrometry-based proteomic techniques are powerful tools for large-scale analysis of protein modifications and have been utilized in a wide range of biomedical research areas to uncover novel biomarkers. Our topic was designed to foster insights into advances in the application of proteomics for early prenatal screening of GDM. We ultimately ended up with nine research papers.

Blood proteomics for early prenatal screening of GDM

Zhao et al[3] obtained blood samples from pregnant females during the 12-16 wk of gestation. Thirty of these pregnant females were later diagnosed with GDM at 24 to 28 wk of gestation and chosen as the subjects for the study. Using iTRAQ analysis, 33 differentially expressed proteins (DEPs) were identified in the serum samples. This study highlights the roles of the complement system, blood coagulation, and the inflammatory and immune responses in the pathogenesis of GDM. Specifically, the authors noted that a panel of four candidate proteins (insulin-like growth factor-binding protein 5, fibrinogen alpha chain, coagulation factor IX, and apolipoprotein E) were able to distinguish women who later developed GDM from controls, demonstrating excellent sensitivity and specificity. Another nested case‒control study was conducted by Ravnsborg et al[4], who applied a proteomics approach to first-trimester sera from 60 obese women. Serum proteomic profiling revealed 25 proteins whose levels significantly differed between the cases and controls. The more significant finding of this study was that three proteins, vitronectin, serum amyloid P-component, and afamin, were further confirmed as biomarkers of GDM. Shen et al[5] reported that 31 and 27 DEPs were identified in the serum samples collected from pregnant females later diagnosed with GDM during 12-16 wk of gestation and GDM patients at 24-28 wk. These proteins were linked to diabetes and maternal and perinatal complications. This study highlights the roles of the blood clotting cascade and the complement system in the development of GDM. DEPs could serve as potential biomarkers for prediction and diagnosis of GDM in the future. In the study of Mavreli et al[6], plasma samples collected from five women who developed GDM and five nondiabetic women in the first trimester were analysed using iTRAQ-based proteomics. Enzyme-linked immunosorbent assay was used in an independent cohort of 25 patients with GDM and 25 normal controls for verification. Notable differences in expression were observed between the two groups for thrombospondin-4, basement membrane-specific heparan sulfate proteoglycan core protein, extracellular matrix protein 1, beta-ala-his dipeptidase, and prenylcysteine oxidase 1. These DEPs are primarily linked to complement and coagulation cascades.

Placental proteomics for early prenatal screening of GDM

Using data-independent acquisition proteomics, Wei et al[7] conducted a study to compare the protein expression profiles of placental tissue between patients with GDM and healthy controls. They identified 37 proteins that were differently expressed between the two groups. This study provided preliminary evidence that CD248 and CD109 can serve as predictive markers for GDM during early pregnancy. Ge et al[8] used iTRAQ for proteomic screening of DEPs in the placenta from GDM patients and healthy women. A total of 68 DEPs in the placenta of GDM patients were identified, comprising 47 downregulated and 21 upregulated DEPs. Fourteen specific proteins (EXOSC7, PHGDH, SPTBN2L, ANK1, SPTB, RAB21, CIRBP, SLC4A1, RAB3B, EPPK1, HNRNPA3, HNRNP, HNRNPAB, and HNRNPA2B1) were found to be differentially expressed in the placenta. These proteins may play a crucial role in regulating the occurrence and development of GDM through multilink and multichannel regulation. Chen et al[9] investigated the protein expression profiles of placental samples from 89 women with GDM and 83 with normal glucose tolerance. The study identified 123 DEPs in the placenta involved in oxidative phosphorylation, inflammatory signaling, ribosomal function, and the pancreatic secretion pathway. In addition, the plasma total triglyceride and galectin 3 binding protein levels had good predictive and diagnostic value during early pregnancy.

Urine proteomics for early prenatal screening of GDM

Due to its accessibility in large quantities, noninvasive collection, and easy preparation, urine has become a good biological sample for biomarker identification. Ramachandrarao et al[10] analysed the exosome proteome content in 24-h urine samples obtained at 20 wk of pregnancy from pregnant women with GDM, pregnant women with pregestational type 2 diabetes (PGD), and controls (CTRLs). A total of 856, 734, and 646 proteins in exosomes were identified in the 24-h urine samples from patients in the PGD, GDM, and CTRL groups, respectively. The S100 calcium-binding protein A9 was found to be significantly increased in both PGD and GDM patients. Guo et al[11] collected urine samples from 889 healthy young pregnant women in the early second trimester, 69 of whom were subsequently diagnosed with GDM at 24 to 28 wk of gestation. Through iTRAQ-based quantitative proteomics, a total of 1901 proteins were identified in this study, including 119 significantly DEPs. This study suggested that CD59 and interleukin 1 receptor antagonist (IL1RA) in urine could be early, noninvasive diagnostic predictors of GDM in young pregnant women, and that IL1RA has greater diagnostic power than CD59.

CONCLUSION

Over the past few decades, there has been a growing focus on predicting GDM at an early stage. However, finding "ideal biomarkers" that offer exceptional sensitivity, specificity, and reproducibility to identify women at risk of GDM is still a challenge. Hence, further efforts are required to determine definitive GDM biomarkers.

Footnotes

Provenance and peer review: Invited article; Externally peer reviewed.

Peer-review model: Single blind

Specialty type: Endocrinology and metabolism

Country of origin: China

Peer-review report’s classification

Scientific Quality: Grade B

Novelty: Grade B

Creativity or Innovation: Grade B

Scientific Significance: Grade B

P-Reviewer: Gragnaniello V, Italy S-Editor: Liu JH L-Editor: A P-Editor: Cai YX

References
1.  Cao WL, Yu CP, Zhang LL. Serum proteins differentially expressed in gestational diabetes mellitus assessed using isobaric tag for relative and absolute quantitation proteomics. World J Clin Cases. 2024;12:1395-1405.  [PubMed]  [DOI]  [Cited in This Article: ]  [Reference Citation Analysis (2)]
2.  Yanachkova V, Staynova R, Stankova T, Kamenov Z. Placental Growth Factor and Pregnancy-Associated Plasma Protein-A as Potential Early Predictors of Gestational Diabetes Mellitus. Medicina (Kaunas). 2023;59.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in F6Publishing: 3]  [Reference Citation Analysis (0)]
3.  Zhao D, Shen L, Wei Y, Xie J, Chen S, Liang Y, Chen Y, Wu H. Identification of candidate biomarkers for the prediction of gestational diabetes mellitus in the early stages of pregnancy using iTRAQ quantitative proteomics. Proteomics Clin Appl. 2017;11.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 14]  [Cited by in F6Publishing: 19]  [Article Influence: 2.7]  [Reference Citation Analysis (0)]
4.  Ravnsborg T, Svaneklink S, Andersen LLT, Larsen MR, Jensen DM, Overgaard M. First-trimester proteomic profiling identifies novel predictors of gestational diabetes mellitus. PLoS One. 2019;14:e0214457.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in F6Publishing: 444]  [Reference Citation Analysis (0)]
5.  Shen L, Zhao D, Chen Y, Zhang K, Chen X, Lin J, Li C, Iqbal J, Zhao Y, Liang Y, Wei Y, Feng C. Comparative Proteomics Analysis of Serum Proteins in Gestational Diabetes during Early and Middle Stages of Pregnancy. Proteomics Clin Appl. 2019;13:e1800060.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 12]  [Cited by in F6Publishing: 14]  [Article Influence: 2.8]  [Reference Citation Analysis (0)]
6.  Mavreli D, Evangelinakis N, Papantoniou N, Kolialexi A. Quantitative Comparative Proteomics Reveals Candidate Biomarkers for the Early Prediction of Gestational Diabetes Mellitus: A Preliminary Study. In Vivo. 2020;34:517-525.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 16]  [Cited by in F6Publishing: 16]  [Article Influence: 4.0]  [Reference Citation Analysis (0)]
7.  Wei Y, He A, Huang Z, Liu J, Li R. Placental and plasma early predictive biomarkers for gestational diabetes mellitus. Proteomics Clin Appl. 2022;16:e2200001.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in F6Publishing: 3]  [Reference Citation Analysis (0)]
8.  Ge L, Huang P, Miao H, Yu H, Wu D, Chen F, Lin Y, Lin Y, Li W, Hua J. The new landscape of differentially expression proteins in placenta tissues of gestational diabetes based on iTRAQ proteomics. Placenta. 2023;131:36-48.  [PubMed]  [DOI]  [Cited in This Article: ]  [Reference Citation Analysis (0)]
9.  Chen F, Li M, Fei X, Chen X, Zhang Z, Zhu W, Shen Y, Mao Y, Liu J, Xu J, Du J. Predictive plasma biomarker for gestational diabetes: A case-control study in China. J Proteomics. 2023;271:104769.  [PubMed]  [DOI]  [Cited in This Article: ]  [Reference Citation Analysis (0)]
10.  Ramachandrarao SP, Hamlin AA, Awdishu L, Overcash R, Zhou M, Proudfoot J, Ishaya M, Aghania E, Madrigal A, Kokoy-Mondragon C, Kao K, Khoshaba R, Bounkhoun A, Ghassemian M, Tarsa M, Naviaux RK. Proteomic analyses of Urine Exosomes reveal New Biomarkers of Diabetes in Pregnancy. Madridge J Diabetes. 2016;1:11-22.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 10]  [Cited by in F6Publishing: 10]  [Article Influence: 1.3]  [Reference Citation Analysis (0)]
11.  Guo Y, Han Z, Guo L, Liu Y, Li G, Li H, Zhang J, Bai L, Wu H, Chen B. Identification of urinary biomarkers for the prediction of gestational diabetes mellitus in early second trimester of young gravidae based on iTRAQ quantitative proteomics. Endocr J. 2018;65:727-735.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 10]  [Cited by in F6Publishing: 8]  [Article Influence: 1.3]  [Reference Citation Analysis (0)]