Prescribing Trajectories in Type 2 Diabetes in the United States, 2019-2024.

importanceClinical guidelines for type 2 diabetes (T2D) provide population-level recommendations, but real-world treatment patterns evolve dynamically and vary across patients. Understanding longitudinal prescribing trajectories may reveal heterogeneity in care not captured by cross-sectional analyses.

objectiveTo identify and characterise real-world T2D prescribing trajectories using nationwide electronic health record (EHR) data. DESIGN, SETTING AND

participantsThis retrospective cohort study used de-identified EHR data from the TriNetX Research Network. Adults newly diagnosed with T2D who initiated their first glucose-lowering medication in the first half of 2019 were followed from 2019 through 2024. EXPOSURES: Longitudinal glucose-lowering prescribing trajectories derived from prescription orders across nine drug classes (metformin, sulfonylureas, thiazolidinediones, dipeptidyl peptidase-4 inhibitors, sodium-glucose cotransporter 2 inhibitors, glucagon-like peptide-1 RAs (GLP-1 RAs), dual glucose-dependent insulinotropic polypeptide/GLP-1 RAs (GIP/GLP-1 RAs), insulin and other antidiabetic agents), encoded in 12 semiannual intervals and identified using hierarchical agglomerative clustering. MAIN OUTCOMES AND MEASURES: Prescribing trajectory cluster membership; adjusted longitudinal changes in body mass index (BMI) and haemoglobin A1c (HbA1c); and associations between cluster membership and patient characteristics assessed using multivariable logistic regression.

resultsAmong 9327 patients, 30 distinct prescribing trajectory clusters were identified and grouped into monotherapy, dual therapy, complex therapy, variant therapy and GLP-1 RA-based trajectories. Treatment intensification patterns, BMI and HbA1c trajectories and demographic composition varied substantially across clusters. Within GLP-1 RA-based trajectories (11 clusters), substantial within-group reductions in BMI and HbA1c were observed; these clusters generally comprised patients with higher baseline BMI and most commonly involved transitions following metformin. GLP-1 RA prescribing was more frequent among younger patients and those with higher baseline BMI. In early GLP-1 RA transition trajectories, Asian and Hispanic patients had lower odds of cluster membership compared with non-Hispanic White patients (Asian: odds ratio [OR] 0.40; 95% CI 0.23-0.63; Hispanic: OR 0.57; 95% CI 0.40-0.79). CONCLUSIONS AND RELEVANCE: 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, underscoring the value of longitudinal approaches for evaluating real-world diabetes care patterns.