Anthropometric metabolic subtypes and health outcomes: A data‐driven cluster analysis
Anthropometric metabolic subtypes identified with easily accessible parameters reflecting multifaceted pathology of overweight and obesity were associated with distinct risks of long-term adverse health outcomes.
Authors
- Xiaoyun Yang (ORCID: https://orcid.org/0000-0002-7326-4387)
- Xiaohui Ma (ORCID: https://orcid.org/0009-0003-3880-5752)
- Li Ding (ORCID: https://orcid.org/0000-0001-7982-4157)
- Gang Hu (ORCID: https://orcid.org/0000-0002-6172-8017)
- Lina Chang (ORCID: https://orcid.org/0000-0002-2425-1272)
- Ming Rong Liu (ORCID: https://orcid.org/0000-0003-2665-4072)
- Jiaxing Wang (ORCID: https://orcid.org/0000-0002-7672-6911)
- Yuxin Fan (ORCID: https://orcid.org/0000-0003-1561-1472)
- Qing He (ORCID: https://orcid.org/0000-0003-1806-2675)
Institutions
- Pennington Biomedical Research Center (US)
- Tianjin Medical University General Hospital (CN)
Publication Details
- Journal
- Diabetes Obesity and Metabolism
- Published
- 2025-02-28
- DOI
- https://doi.org/10.1111/dom.16299
- Citations
- 1
- Primary Topic
- Diet and metabolism studies
- Type
- article
- Field-Weighted Citation Impact
- 1.16
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China
- National Institute of General Medical Sciences