Analysis reveals high accuracy of polygenic risk scores in predicting type 2 diabetes, suggesting personalized prevention strategies.
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.