A Multi-Polygenic Risk Score Approach Incorporating Physical Activity Genotypes for Predicting Type 2 Diabetes and Associated Comorbidities: A FinnGen Study

Aims/hypothesis

Genetic prediction of type 2 diabetes risk has proven difficult using current methods. Recent studies have shown that genetic variants associated with physical activity behavior are linked to type 2 diabetes incidence. This study investigated how a polygenic risk score (PRS) for type 2 diabetes relates to the incidence of type 2 diabetes and its comorbidities and whether incorporating genetic risk from physical activity-related traits and measured lifestyles improves prediction. We hypothesized that adding physical activity genotypes into prediction models would improve predictive accuracy.

Methods

PRSs were calculated for 279,373 Finns in the FinnGen cohort (average age 62 years, 52% women). Cox proportional hazards models were used with follow-up from birth. In addition, we assessed whether predictive ability (concordance index) improved when PRSs for physical activity, sedentary time, cardiorespiratory fitness, muscle strength, and body mass index were included alongside the type 2 diabetes PRS. Finally, we assessed how smoking and body mass index changed the model’s predictive ability.

Results

Each standard deviation unit increase in the type 2 diabetes PRS was associated with an 8% higher risk of developing type 2 diabetes. Among individuals with type 2 diabetes, the PRS was linked to higher risks of comorbidities: 4% higher for nephropathy and retinopathy and 5% for severe cardiovascular disease, but not neuropathy. Physical activity-related PRSs were also independently associated with the risk of type 2 diabetes—lower risk for physical activity (7%), cardiorespiratory fitness (6%), and muscle strength (4%) and higher risk for sedentary time (14%) and body mass index (35%). However, physical activity-related PRSs did not significantly improve the model’s concordance index (0.644 before vs. 0.672 after adding all other PRSs). In contrast, including body mass index and smoking status increased predictive ability (c-index 0.744).

Conclusions and applicability

PRSs for type 2 diabetes and physical activity-related phenotypes independently predict the incidence of type 2 diabetes and comorbidities. However, adding physical activity-related scores to the model does not significantly improve prediction beyond the type 2 diabetes score. Notably, the PRS for body mass index was better than the PRS for type 2 diabetes in predicting type 2 diabetes incidence. These findings support the hypothesis that genetic pleiotropy may partially explain associations between type 2 diabetes and physical activity behavior.

Summary boxes

What is already known about this subject?

• Genetic factors contribute substantially to the risk of type 2 diabetes, but it is primarily a multifactorial condition in which modifiable lifestyle factors—including physical activity—play a critical role in onset and progression.

• Genetic variants related to physical activity behavior have been associated with type 2 diabetes.

• The clinical utility of polygenic risk scores in predicting type 2 diabetes risk remains limited, as they explain only a small proportion of genetic variance and provide minimal improvement in risk prediction beyond established clinical risk factors.

What is the key question?

• Can the risk estimates for type 2 diabetes and its comorbidities be improved by incorporating genetic risk factors associated with physical activity and measured lifestyle behaviors?

What are the new findings?

• Polygenic risk scores for both type 2 diabetes and physical activity-related phenotypes independently predict the incidence of type 2 diabetes and its comorbidities.

• Incorporating physical activity-related polygenic risk scores into the model does not significantly improve predictive accuracy beyond the type 2 diabetes risk score alone.

• The findings support the hypothesis that genetic pleiotropy may partially explain associations between type 2 diabetes and physical activity behavior.

How might this impact on clinical practice in the foreseeable future?

• Although polygenic risk scores for type 2 diabetes may aid in identifying high-risk individuals for targeted prevention, their integration into clinical practice requires further validation.