Intricacies during pregnancy with gestational diabetes mellitus

Abstract

The study by Cao et al aimed to identify early second-trimester biomarkers that could predict gestational diabetes mellitus (GDM) development using advanced proteomic techniques, such as Isobaric tags for relative and absolute quantitation isobaric tags for relative and absolute quantitation and liquid chromatography-mass spectrometry liquid chromatography-mass spectrometry. Their analysis revealed 47 differentially expressed proteins in the GDM group, with retinol-binding protein 4 and angiopoietin-like 8 showing significantly elevated serum levels compared to controls. Although these findings are promising, the study is limited by its small sample size (n = 4 per group) and lacks essential details on the reproducibility and reliability of the protein quantification methods used. Furthermore, the absence of experimental validation weakens the interpretation of the protein-protein interaction network identified through bioinformatics analysis. The study's focus on second-trimester biomarkers raises concerns about whether this is a sufficiently early period to implement preventive interventions for GDM. Predicting GDM risk during the first trimester or pre-conceptional period may offer more clinical relevance. Despite its limitations, the study presents valuable insights into potential GDM biomarkers, but larger, well-validated studies are needed to establish their predictive utility and generalizability.

Key Words: Gestational diabetes mellitus; Biomarkers; Differentially expressed proteins; Retinol-binding protein 4; Angiopoietin-like 8; Proteomics; Lifestyle interventions; Early prediction

Core Tip: The letter highlights certain limitations of the study by Cao et al, which explored early second-trimester biomarkers, retinol-binding protein 4 and angiopoietin-like 8, for predicting gestational diabetes mellitus (GDM). While valuable, identifying GDM in the early second trimester may not provide sufficient time for pregnant mothers to adopt major lifestyle interventions. Early first-trimester prediction or pre-conceptional identification would be more clinically significant. While the study uses advanced proteomic techniques, including isobaric tags for relative and absolute quantitation and liquid chromatography-mass spectrometry, to identify differentially expressed proteins, it is constrained by a small sample size and methodological gaps. Specifically, the reliability of protein quantification and high-abundance protein removal is unclear, and the lack of experimental validation limits the functional interpretation of identified protein interactions.

TO THE EDITOR

Gestational diabetes mellitus (GDM) is a significant global health concern, affecting 7%-10% of pregnancies, and posing risks for both maternal and fetal complications. Its pathophysiology is quite intricate and requires newer diagnostic, screening, and management techniques to keep mother and child health a priority.

GDM

GDM is a major public health problem. It is estimated that GDM affects around 7%–10% of all pregnancies globally. The diagnosis of GDM is primarily based on an oral glucose tolerance test performed usually between 24-28 weeks of gestation. Identification and prompt management are essential, as uncontrolled hyperglycemia in pregnant mothers with GDM often leads to adverse maternal and fetal outcomes[1]. GDM can ultimately lead to significant development of diabetic retinopathy (DR) during pregnancy or aggravation of an already active DR resulting in impairment of vision[2]. Besides, nearly 50% of women with GDM later go on to acquire type 2 diabetes mellitus.

Considering the plethora of short-term and long-term repercussions, lifestyle interventions introduced before pregnancy or even early in pregnancy have the potential to prevent GDM development[3]. Hence, identifying women at a high risk of developing GDM is of paramount importance.

Cao et al[4] have attempted to identify potential biomarkers in pregnant women in their early second trimester (12-16 weeks) that can help predict the subsequent development of GDM. While their findings contribute to our understanding of GDM, it is unclear if identifying GDM in the early second trimester would provide sufficient time for pregnant mothers to adopt major lifestyle interventions that can avert the subsequent development of GDM. On the contrary, it would be clinically more relevant if the subsequent development of GDM could be predicted in the early first trimester or even in the pre-conceptional period, which could allow for timely preventive measures.

The study[4] involves the utilization of advanced proteomic techniques that allow comprehensive analysis of protein expression alterations associated with GDM. Isobaric tags for relative and absolute quantitation and liquid chromatography-mass spectrometry facilitate high-throughput identification and quantification of differentially expressed proteins (DEPs), providing valuable insights into the molecular mechanisms triggering GDM pathophysiology. Furthermore, the inclusion of bioinformatics analysis augments the understanding of the proteomic data by elucidating the functional importance of the recognized proteins. Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and protein-protein interaction network construction contribute to a deeper understanding of the biological processes and pathways involved in GDM[5]. Although 47 DEPs were identified in the GDM group, serum levels of only retinol-binding protein 4 (RBP4) and angiopoietin-like 8 (ANGPTL8) were found to be significantly higher in the GDM as compared to the non-GDM control group. The authors thus conclude that RBP4 and ANGPTL8 may be early predictors of GDM.

Although praiseworthy, the study[4] is markedly limited by its small sample size (only four mothers each with and without GDM). Besides, specific methodological issues deserve mention. First, while the methodology mentions the use of the Bradford method for protein quantification and sodium dodecyl sulfate-polyacrylamide gel electrophoresis for quality assessment, it lacks detailed information about the reproducibility and reliability of these methods. Second, the methodology mentions the removal of high-abundance proteins. Still, it does not specify which proteins were removed that might account for potential bias introduced by incomplete removal or unintended protein loss. Third, although the protein interaction network provides insights into possible interactions among DEPs, the interpretation of these interactions is limited by the lack of experimental validation. Without empirical evidence confirming the interactions, the functional significance of these networks remains uncertain. The bioinformatics analysis, including GO functional annotation, KEGG pathway analysis, and protein-protein interaction network construction, relies on computational algorithms and databases. It is essential to acknowledge the likely drawbacks and biases linked with these tools and provide validation strategies to confirm the biological relevance of the findings. Lastly, while enzyme-linked immunosorbent assay validation confirms the upregulation of RBP4 and ANGPTL8 in GDM patients, the small sample size and demographic restrictions raise concerns about the generalizability of these findings.

CONCLUSION

In conclusion, while the study by Cao et al[4] provides valuable insights into potential biomarkers for GDM, its limitations, particularly the small sample size, insufficient methodological details, and lack of experimental validation, should be addressed in future studies. Additionally, earlier prediction of GDM, potentially in the first trimester or pre-conception, would offer more time for lifestyle interventions and could enhance the clinical relevance of such findings.