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...

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Principais autores:	Tatsuhiko Naito, Kosuke Inoue, Shinichi Namba, Kyuto Sonehara, Ken Suzuki, BioBank Japan, Koichi Matsuda, Naoki Kondo, Tatsushi Toda, Toshimasa Yamauchi, Takashi Kadowaki, Yukinori Okada
פורמט:	Artigo
שפה:	Inglês
יצא לאור:	Nature Portfolio 2024-09-01
סדרה:	Communications Medicine
גישה מקוונת:	https://doi.org/10.1038/s43856-024-00596-7
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