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 t...

Ausführliche Beschreibung

Bibliografische Detailangaben

Hauptverfasser:	Tatsuhiko Naito, Kosuke Inoue, Shinichi Namba, Kyuto Sonehara, Ken Suzuki, BioBank Japan, Koichi Matsuda, Naoki Kondo, Tatsushi Toda, Toshimasa Yamauchi, Takashi Kadowaki, Yukinori Okada
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	Nature Portfolio 2024-09-01
Schriftenreihe:	Communications Medicine
Online-Zugang:	https://doi.org/10.1038/s43856-024-00596-7
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