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.
Keywords: Adiponectin; Biomarkers; Complement and coagulation cascade; Gestational diabetes mellitus; Machine learning; Multiple reaction monitoring.
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