Assessing individual genetic susceptibility to metabolic syndrome: interpretable machine learning method
This study established a reliable XGBoost-based GRS model and a GRS prediction platform (https://metabolicsyndromeapps.shinyapps.io/geneticriskscore/) to assess individual genetic susceptibility to MetS. This model has high interpretability and can provide personalized reference for determining the necessity of primary prevention measures for MetS. Additionally, there may be interactions between traditional risk factors and GRS, and the integration of both in a comprehensive model is useful in the prediction of MetS occurrence.
Authors
- Simin Wang (ORCID: https://orcid.org/0000-0001-6135-7546)
- Bizhen Gao (ORCID: https://orcid.org/0000-0002-3792-7761)
- Yuanyuan Li (ORCID: https://orcid.org/0000-0002-0973-2481)
- Shijie Qiao
- Xirui Huang
- Wenhui Xiong (ORCID: https://orcid.org/0009-0008-1228-2400)
- Xiujuan Zheng (ORCID: https://orcid.org/0000-0002-2503-1634)
- Menghan Yang
- Tao Huang (ORCID: https://orcid.org/0009-0001-4998-6021)
Institutions
- Fujian University of Traditional Chinese Medicine (CN)
Publication Details
- Journal
- Annals of Medicine
- Published
- 2025-06-22
- DOI
- https://doi.org/10.1080/07853890.2025.2519679
- Citations
- 2
- Primary Topic
- Metabolomics and Mass Spectrometry Studies
- Type
- article
- Field-Weighted Citation Impact
- 1.61
Funders
- National Natural Science Foundation of China