Pathway insights and predictive modeling for type 2 diabetes using polygenic risk scores

Area of Science:

• Genetics
• Epidemiology
• Precision Medicine

Background:

• Type 2 diabetes (T2D) represents a substantial global health challenge.
• Effective risk prediction models are crucial for early intervention and prevention.

Purpose of the Study:

• To develop and validate a polygenic risk score (PRS) model integrated with clinical data for predicting T2D risk.
• To assess the PRS model's performance and clinical utility in a large Taiwanese cohort.

Main Methods:

• Analysis of electronic medical records from a major Taiwanese medical center (315,424 T2D cases, 141,484 controls).
• Construction of a T2D PRS using 14 genome-wide significant SNPs.
• Integration of PRS with clinical data for predictive modeling and validation in the Taiwan Biobank.
• Phenome-wide association study (PheWAS) and pathway analysis.

Main Results:

• The integrated PRS model achieved high predictive accuracy (AUROC 0.842).
• A clinical risk score (0-19) was established.
• PRS was associated with T2D complications (e.g., diabetic retinopathy, hypertension) and biological pathways (e.g., IL-15 production, WNT/β-catenin signaling).

Conclusions:

• The developed PRS model offers a powerful tool for personalized T2D risk assessment.
• Integration of genetic and clinical data enhances T2D prediction accuracy.
• Findings support the use of PRS in early T2D prevention strategies.