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Polygenic Score for T2D Prediction in Indigenous Cohorts


Diabetologia


Polygenic Score for T2D Prediction in Indigenous Cohorts

Resumen

This study evaluates the contribution of polygenic scores (PS) based on genome-wide association studies (GWAS) in predicting the incidence of type 2 diabetes among Indigenous populations in the Southwestern USA. The research focuses on three distinct cohorts: birth, youth, and adults. Results show that the PS, constructed from European-ancestry GWAS data, significantly enhances predictive accuracy when combined with clinical variables such as BMI, HbA1c, and fasting plasma glucose (FPG). Furthermore, the study highlights that genetic effects on diabetes risk are more pronounced in younger age groups, indicating that early-life genetic screening could aid in preventive strategies. The decision curve analysis revealed that adding PS information can improve risk stratification and guide preventive interventions, especially in populations where clinical predictors are limited.

Diseño del Estudio

Intervenciones

Genetic risk assessmentPolygenic score analysisClinical variable integration

Tipo de Estudio

Cohort

Resultados

Type 2 diabetes incidence predictionRisk reclassification improvementClinical decision-making support

Duración y Tamaño

Largo plazo (1–5 años)
Tamaño grande (500–5000)

Población del Estudio

Geografía

North America

Metodología

The study utilized a longitudinal design involving 7701 genotyped participants from an Indigenous population. Three cohorts were analyzed, with genotypic data imputed against whole genome sequence data. PS construction involved selecting significant genome-wide variants.

Statistical analyses included Cox regression, ROC curves for AUC assessment, and NRI calculations. Decision curve analysis assessed the clinical utility of incorporating PS into risk prediction models, showing improved prediction in younger cohorts.

Intervenciones

The intervention involved calculating polygenic scores for type 2 diabetes using various GWAS-derived variant sets. The DIAGRAM 2018 PS showed the highest predictive value across cohorts.

Hallazgos Clave

The DIAGRAM 2018 PS significantly improved type 2 diabetes risk prediction when combined with clinical variables. Genetic factors were more predictive in younger cohorts, suggesting early screening benefits. The most notable improvements were observed in the birth cohort.

Comparación con otros Estudios

Compared to previous European-ancestry studies, this research highlighted the effective cross-population application of PS. The study underscores PS's potential for broader applications in diverse populations, especially for early risk identification.

Referencia de la Revista

Wedekind LE, Mahajan A, Hsueh WC, et al. The utility of a type 2 diabetes polygenic score in addition to clinical variables for prediction of type 2 diabetes incidence in birth, youth and adult cohorts in an Indigenous study population. Diabetologia. 2023;66(4):847-860. doi:10.1007/s00125-023-05870-2

Relacionados y Discusiones

Key References

Most relevant evidence and guidance related to this research.

1
Expert

Genetic Screening's Potential to Reduce Early Disease Deaths

Professor Sir Peter Donnelly
2
Expert

Advancements in Polygenic Risk Scores for Diverse Populations

Dr. Kári Stefánsson
3
Expert

AI Tool Predicts Type 2 Diabetes Risk Years in Advance

NHS England

30 total sources in this category