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

SUBMITTER: Naito T 

PROVIDER: S-EPMC11415376 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature