Predicting responsiveness to GLP-1 pathway drugs using real-world data
These findings indicate a thorough analysis of real-world electronic health records could reveal new information to improve treatment decisions for the treatment of T2D. The predictive model developed in this study highlights the importance of considering individual patient characteristics and medication interactions when prescribing GLP-1 M drugs.
Authors
- Xiaodong Zhu (ORCID: https://orcid.org/0000-0002-6535-4606)
- MAUREEN A. GANNON (ORCID: https://orcid.org/0000-0003-3287-8101)
- Quinn S. Wells (ORCID: https://orcid.org/0000-0003-3899-0313)
- John M. Stafford (ORCID: https://orcid.org/0000-0001-6527-959X)
- Michael J. Fowler
Institutions
- Tennessee Valley Authority (US)
- Vanderbilt University (US)
- VA Tennessee Valley Healthcare System (US)
- Vanderbilt University Medical Center (US)
Publication Details
- Journal
- BMC Endocrine Disorders
- Published
- 2024-12-18
- DOI
- https://doi.org/10.1186/s12902-024-01798-9
- Citations
- 13
- Primary Topic
- Diabetes Treatment and Management
- Type
- article
- Field-Weighted Citation Impact
- 4.65
Funders
- U.S. Department of Veterans Affairs
- American Heart Association
- National Institutes of Health