Machine learning reveals heterogeneous associations between environmental factors and cardiometabolic diseases across polygenic risk scores.
Tatsuhiko NaitoKosuke InoueShinichi NambaKyuto SoneharaKen Suzukinull nullKoichi MatsudaNaoki KondoTatsushi TodaToshimasa YamauchiTakashi KadowakiYukinori OkadaPublished in: Communications medicine (2024)
Our study highlights the importance of individual-level prediction of disease risks associated with target exposure in precision medicine.