Pathway insights and predictive modeling for type 2 diabetes using polygenic risk scores
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 medica...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-13391-8 |
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| Summary: | 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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| ISSN: | 2045-2322 |