Prescribing Trajectories in Type 2 Diabetes in the United States, 2019–2024
Authors' conclusion
Data-driven clustering of longitudinal EHR medication data identifies substantial heterogeneity in real-world T2D treatment trajectories. Differences in the timing and adoption of newer therapies were observed across demographic groups
Study context
- Population
- Adults newly diagnosed with T2D who initiated their first glucose-lowering medication in the first half of 2019, TriNetX Research Network, followed 2019–2024
- Participants
- 9,327 adults
- Outcome
- Prescribing trajectory cluster membership; adjusted longitudinal changes in BMI and HbA1c; associations between cluster membership and patient characteristics (multivariable logistic regression)
Methodological detailsnationwide electronic health record (EHR) data, hierarchical agglomerative clustering
- Nationwide EHR data
- Hierarchical agglomerative clustering
Main finding
As reported in the abstract
Prescribing trajectory clusters
- Distinct prescribing trajectory clusters, grouped into monotherapy, dual therapy, complex therapy, variant therapy and GLP-1 RA-based trajectories
- 30
Observational data: associations, not effects.
Further findings
| Outcome | OR (95% CI) | |
|---|---|---|
| Membership of early GLP-1 RA transition trajectories by race/ethnicityAsianvs Non-Hispanic White | 0.40 (0.23–0.63) | |
| Membership of early GLP-1 RA transition trajectories by race/ethnicityHispanicvs Non-Hispanic White | 0.57 (0.40–0.79) | |
| 0.20.5125Odds ratio (log scale) |
GLP-1 RA-based trajectories
- GLP-1 RA-based trajectory clusters
- 11
- Substantial within-group reductions in body mass index (BMI) and haemoglobin A1c (HbA1c); generally higher baseline BMI; most commonly transitions following metformin
- GLP-1 RA prescribing more frequent among younger patients and those with higher baseline BMI
Original publication
Prescribing Trajectories in Type 2 Diabetes in the United States, 2019–2024. Diabetes Obes Metab. Published 17 June 2026. doi:10. 1111/ dom. 71013
Composed by AI from the published abstract and checked against it. How Briefs are made →