Diabetes PreventionPrecision MedicineDigital Health

Polygenic Score for T2D Prediction in Indigenous Cohorts


Diabetologia


Polygenic Score for T2D Prediction in Indigenous Cohorts

Summary

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.

Study Design

Interventions

Genetic risk assessmentPolygenic score analysisClinical variable integration

Study Type

Cohort

Outcomes

Type 2 diabetes incidence predictionRisk reclassification improvementClinical decision-making support

Duration and Size

Long-Term (1–5 y)
Large size (500–5000)

Study Population

Age Range

Children (≤13)Adolescent (13–18)Young Adult (19–39)Middle Aged (40-64)

Sex

MaleFemale

Geography

North America

Other Criteria

with T2 Diabetes

Methodology

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.

Interventions

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.

Key Findings

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.

Comparison with other Studies

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.

Journal Reference

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

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