Identifying Four Obesity Axes Through Integrative Multiomics and Imaging Analysis
This study aimed to address potential limitations of BMI by exploring the heterogeneity of obesity using magnetic resonance imaging-derived fat distribution and muscle volume measures. We sought to identify distinct obesity axes and investigate their genetic, metabolic, and disease associations. Four obesity axes were identified (general obesity, muscle dominant, peripheral fat, and lower-body fat), each linked to unique genetic loci, metabolic traits, and disease risks. These findings emphasize the potential to extend beyond BMI in defining and managing obesity, offering a more nuanced framework for understanding and treating obesity-related diseases.
Authors
- Brandon Whitcher (ORCID: https://orcid.org/0000-0002-6452-2399)
- Marjola Thanaj (ORCID: https://orcid.org/0000-0002-1789-7112)
- Elena P. Sorokin (ORCID: https://orcid.org/0000-0001-8957-8869)
- Jimmy D. Bell (ORCID: https://orcid.org/0000-0003-3804-1281)
- Madeleine Cule (ORCID: https://orcid.org/0000-0002-7400-5643)
- E. Louise Thomas (ORCID: https://orcid.org/0000-0003-4235-4694)
- Hanieh Yaghootkar (ORCID: https://orcid.org/0000-0001-9672-9477)
- Chiemela S. Odoemelam (ORCID: https://orcid.org/0000-0002-9622-2075)
- Afreen Naz (ORCID: https://orcid.org/0000-0002-8218-5584)
Institutions
- Enzo Life Sciences (United States) (US)
- University of Lincoln (GB)
- University of Glasgow (GB)
- University of Westminster (GB)
Publication Details
- Journal
- Diabetes
- Published
- 2025-04-24
- DOI
- https://doi.org/10.2337/db24-1103
- Citations
- 5
- Primary Topic
- Genetic Associations and Epidemiology
- Type
- article
- Field-Weighted Citation Impact
- 9.09
Funders
- Diabetes UK