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

Objectives

Gestational diabetes mellitus (GDM) is a common metabolic disorder characterized by insulin resistance and systemic inflammation. Emerging evidence suggests that the Wnt/β-catenin signaling pathway may play a role in metabolic dysregulation; however, its clinical relevance in GDM remains unclear. This study aimed to evaluate the diagnostic value of Wnt signaling-related biomarkers, including Wnt-inhibitory factor 1 (WIF-1), secreted frizzled-related protein-4 (SFRP-4), and beta-catenin-1 (CTNNB1) in GDM.

Methods

This case-control study included 60 patients with GDM and 60 healthy pregnant controls. Serum levels of WIF-1, SFRP-4, and CTNNB1 were measured and compared between groups. Receiver operating characteristic (ROC) and multivariable logistic regression assessed diagnostic performance and predictors, while correlation analysis and principal component analysis (PCA) evaluated biomarker relationships.

Results

Serum levels of WIF-1, SFRP4, and CTNNB1 were significantly higher in the GDM group (all p < 0.001). ROC analysis showed moderate diagnostic performance for individual biomarkers, with CTNNB1 demonstrating the highest discriminative ability. The combined biomarker model significantly improved diagnostic accuracy, yielding the highest area under the curve (AUC), sensitivity, and specificity. In multivariable analysis, all three biomarkers remained independently associated with GDM. Correlation analysis revealed moderate interrelationships, with SFRP4 acting as a central component. PCA demonstrated partial separation between GDM and control groups, supporting the ability of Wnt signaling-related biomarkers to capture disease-associated biological variation.

Conclusions

Wnt signaling-related biomarkers, including WIF-1, SFRP4, and CTNNB1, are significantly elevated in GDM and show promising diagnostic value. The combined biomarker approach provides superior discriminative performance compared to individual markers, highlighting its potential role in improving risk stratification and personalized management.