Machine learning reveals heterogeneous associations between environmental factors and cardiometabolic diseases across polygenic risk scores

Abstract

Background: Although polygenic risk scores (PRSs) are expected to be helpful in precision medicine, it remains unclear whether high-PRS groups are more likely to benefit from preventive interventions for diseases. Recent methodological advancements enable us to predict treatment effects at the individual level. Methods: We employed causal forest to explore the relationship between PRSs and individual risk of diseases associated with certain environmental factors. Following simulations illustrating its performance, we applied our approach to investigate the individual risk of cardiometabolic diseases, including coronary artery diseases (CAD) and type 2 diabetes (T2D), associated with obesity and smoking among individuals from UK Biobank (UKB; n = 369, 942) and BioBank Japan (BBJ; n = 149, 421). Results: Here we find the heterogeneous association of obesity and smoking with diseases across PRS values, complicated by the multi-dimensional combination of individual characteristics such as age and sex. The highest positive correlations of PRSs and the exposure-related disease risks are observed between obesity and T2D in UKB and between smoking and CAD in BBJ (Spearman’s ρ = 0.61 and 0.32, respectively). However, most relationships are weak or negative, suggesting that high-PRS groups will not necessarily benefit most from environmental factor prevention. Conclusions: Our study highlights the importance of individual-level prediction of disease risks associated with target exposure in precision medicine.