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September 10, 2025Open Access

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

Key Points

The integrated predictive model achieved high accuracy with an AUROC of 0.842, indicating its effectiveness in predicting type 2 diabetes risk.
Fourteen genome-wide significant SNPs were identified and used to construct the polygenic risk score model, demonstrating a robust genetic basis for prediction.
Electronic medical records from 315,424 cases and 141,484 controls were analyzed to enrich the dataset and validate the findings.
The study supports using polygenic risk scores for early prevention strategies and personalized risk assessment in type 2 diabetes.

Abstract

Type 2 diabetes (T2D) poses a significant global health burden. We developed a polygenic risk score (PRS) model based on genome-wide association study (GWAS) findings and integrated it with clinical data to predict T2D risk. This study analyzed electronic medical records from a major medical center in Taiwan, comprising 315,424 T2D cases and 141,484 controls. Fourteen genome-wide significant SNPs were identified and used to construct the T2D PRS. The integrated predictive model showed high accuracy (AUROC 0.842) and was validated in the Taiwan Biobank. A risk score ranging from 0 to 19 was established for clinical use. Phenome-wide association study (PheWAS) revealed links between PRSs and T2D-related complications, such as diabetic retinopathy and hypertension. Pathway analysis highlighted biological processes including IL-15 production and WNT/β-catenin signaling. Our findings support the use of PRSs in personalized T2D risk assessment and early prevention strategies.

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