Machine learning reveals heterogeneous associations between environmental factors and cardiometabolic diseases across polygenic risk scores
Our study highlights the importance of individual-level prediction of disease risks associated with target exposure in precision medicine.
Authors
- Tatsuhiko Naito (ORCID: https://orcid.org/0000-0002-2779-4600)
- Toshimasa Yamauchi (ORCID: https://orcid.org/0000-0003-4827-6404)
- Tatsushi Toda (ORCID: https://orcid.org/0000-0002-0717-1123)
- Yukinori Okada (ORCID: https://orcid.org/0000-0002-0311-8472)
- Shinichi Namba (ORCID: https://orcid.org/0000-0002-7486-3146)
- Takashi Kadowaki (ORCID: https://orcid.org/0000-0002-5428-3582)
- Ken Suzuki (ORCID: https://orcid.org/0000-0003-1065-3593)
- Koichi Matsuda (ORCID: https://orcid.org/0000-0001-7292-2686)
- Kosuke Inoue (ORCID: https://orcid.org/0000-0001-9614-8103)
- Kyuto Sonehara (ORCID: https://orcid.org/0000-0002-4536-1761)
- Naoki Kondo
Institutions
- Kyoto University (JP)
- RIKEN Center for Integrative Medical Sciences (JP)
- Toranomon Hospital (JP)
- The University of Tokyo (JP)
- The University of Osaka (JP)
Publication Details
- Journal
- Communications Medicine
- Published
- 2024-09-20
- DOI
- https://doi.org/10.1038/s43856-024-00596-7
- Citations
- 4
- Primary Topic
- Genetic Associations and Epidemiology
- Type
- article
- Field-Weighted Citation Impact
- 2.20
Funders
- Japan Foundation for Applied Enzymology
- Japan Agency for Medical Research and Development
- Ministry of Education, Culture, Sports, Science and Technology
- Japan Society for the Promotion of Science
- Moonshot Research and Development Program