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

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...

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Published inScientific reports Vol. 15; no. 1; pp. 28956 - 11
Main Authors Liao, Wen-Ling, Yang, Jai-Sing, Liu, Ting-Yuan, Lu, Hsing-Fang, Chang, Ya-Wen, Tsai, Fuu-Jen
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 07.08.2025
Nature Publishing Group
Nature Portfolio
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Summary: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
2045-2322
DOI:10.1038/s41598-025-13391-8