Wnt Signaling-Related Biomarkers in Gestational Diabetes Mellitus: Diagnostic Performance and Integrated Statistical...

Area of Science:

• Endocrinology and Metabolism
• Molecular Biology
• Biomarker Discovery

Background:

• Gestational diabetes mellitus (GDM) is a prevalent metabolic disorder linked to insulin resistance and inflammation.
• The Wnt/β-catenin signaling pathway is implicated in metabolic dysregulation, but its role in GDM is not fully understood.
• Wnt signaling-related molecules like Wnt-inhibitory factor 1 (WIF-1), secreted frizzled-related protein-4 (SFRP-4), and beta-catenin-1 (CTNNB1) are potential biomarkers.

Purpose of the Study:

• To investigate the diagnostic value of WIF-1, SFRP-4, and CTNNB1 in gestational diabetes mellitus (GDM).
• To assess the combined diagnostic performance of these Wnt signaling biomarkers for GDM.
• To explore the relationship between these biomarkers and GDM status.

Main Methods:

• A case-control study involving 60 GDM patients and 60 healthy pregnant controls.
• Serum levels of WIF-1, SFRP-4, and CTNNB1 were quantified.
• Diagnostic performance was evaluated using Receiver Operating Characteristic (ROC) analysis and multivariable logistic regression; biomarker relationships were assessed via correlation and principal component analysis (PCA).

Main Results:

• Serum concentrations of WIF-1, SFRP-4, and CTNNB1 were significantly elevated in the GDM group (p < 0.001).
• Individual biomarkers showed moderate diagnostic ability, with CTNNB1 being the most discriminative.
• The combined biomarker panel significantly enhanced diagnostic accuracy (AUC, sensitivity, specificity) and all biomarkers remained independent predictors of GDM.

Conclusions:

• Wnt signaling biomarkers (WIF-1, SFRP-4, CTNNB1) are significantly upregulated in GDM, indicating their diagnostic potential.
• A combined biomarker strategy offers superior diagnostic performance over individual markers for GDM.
• These findings suggest a role for Wnt signaling in GDM pathogenesis and highlight potential for improved risk stratification and personalized management.