Unsupervised Learning-Derived Complex Metabolic Signatures Refine Cardiometabolic Risk
These metabolic signatures extract comprehensive information from the metabolomic profile while maintaining clarity and interpretability, facilitating clinical translation. The findings emphasize the crucial roles of lipid subpopulations in cardiometabolic risks, encouraging clinicians to take a more nuanced approach to managing blood lipids and balancing different disease risks.
Authors
- Yuxin Ren (ORCID: https://orcid.org/0000-0003-1065-4350)
- Xiang Zhou (ORCID: https://orcid.org/0000-0002-3835-3894)
- Boyang Xiang (ORCID: https://orcid.org/0009-0007-7584-9390)
- Yujia Zhou (ORCID: https://orcid.org/0000-0002-7779-1897)
- Xiaosong Gu (ORCID: https://orcid.org/0000-0001-6398-9352)
- Xiaoqin Yang (ORCID: https://orcid.org/0000-0001-7559-2125)
Institutions
- Soochow University (CN)
- Second Affiliated Hospital of Soochow University (CN)
- Second Affiliated Hospital of Nanjing Medical University (CN)
Publication Details
- Journal
- JACC Advances
- Published
- 2025-02-20
- DOI
- https://doi.org/10.1016/j.jacadv.2025.101620
- Citations
- 4
- Primary Topic
- Metabolomics and Mass Spectrometry Studies
- Type
- article
- Field-Weighted Citation Impact
- 3.22