Polygenic Risk Stratification Methods for Type 2 Diabetes

Smart overview of the Invention

Type 2 diabetes mellitus (T2D) presents a significant health challenge in the United States and other Western countries, with millions of cases remaining undiagnosed. The heritability of T2D, estimated between 25-72%, underscores the importance of genetic factors in disease development. Current screening methods, despite established guidelines, fail to diagnose many cases in a timely manner, highlighting the need for improved risk stratification and resource allocation.

Summary of Invention

The disclosed method utilizes a polygenic score (PGS) derived from over 11,000 T2D-associated genetic variants to enhance existing screening practices. This PGS is developed from a large multi-ancestry sample and aims to provide additional predictive value beyond traditional risk factors such as family history, age, and BMI. The PGS is associated with earlier onset of T2D, hyperglycemia prevalence, and incidence of T2D after one year.

Predictive Utility

The PGS maintains its predictive utility even when adjusted for family history, enhancing disease risk prediction when combined with genetic data. High genetic risk individuals show an earlier age of onset by 18% compared to those with lower risk. A linear relationship between the PGS and T2D onset is observed, indicating significant predictive power.

Clinical Implications

For individuals without a T2D diagnosis, the PGS correlates with increased odds of hyperglycemia and incident T2D at follow-up. It also associates with advanced illness indicators such as insulin treatment and diabetic neuropathy. These findings are consistent across different ancestries, including Hispanic/Latino cohorts, suggesting broad applicability in diverse populations.

Methodology

• Determine presence or absence of at least 5,000 SNPs in a biological sample.
• Calculate a PGS based on these SNPs, optionally weighting each by a coefficient.
• Combine PGS with traditional factors like family history, age, and BMI for comprehensive risk assessment.
• Apply findings to guide treatment decisions, including lifestyle changes and medication administration.