Predicting responsiveness to GLP-1 pathway drugs using real-world data

• Published in: BMC endocrine disorders Vol. 24; no. 1; pp. 269 - 17
• Main Authors: Zhu, Xiaodong, Fowler, Michael J., Wells, Quinn S., Stafford, John M., Gannon, Maureen
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 18.12.2024; BioMed Central; BMC
• Subjects: Aged; Biomarkers - analysis; Codes; Diabetes; Diabetes mellitus (non-insulin dependent); Diabetes Mellitus, Type 2 - drug therapy; Diabetes Mellitus, Type 2 - metabolism; Diabetes therapy; Dipeptidyl-Peptidase IV Inhibitors - therapeutic use; Disease; Drug delivery; Drug interaction; Drug therapy; Electronic health record; Electronic health records; Electronic medical records; Exenatide; Female; GLP-1; Glucagon; Glucagon-Like Peptide 1; Glucagon-Like Peptide-1 Receptor Agonists; Glucose; Glycated Hemoglobin - analysis; Glycated Hemoglobin - metabolism; Glycosylated hemoglobin; HbA1c; Health aspects; Hemoglobin; Humans; Hypoglycemic Agents - therapeutic use; Machine Learning; Male; Medical records; Middle Aged; Neural networks; Nonsteroidal anti-inflammatory drugs; Patients; Prediction models; Predictive model; Prescription drugs; Prognosis; Race; Regression analysis; Thiazolidinediones; Type 2 diabetes; United States > US; Type 2 diabetes; GLP-1; Electronic health record; HbA1c; Predictive model
• ISSN: 1472-6823; 1472-6823
• DOI: 10.1186/s12902-024-01798-9

1. Patient characteristics such as poorer blood glucose control, higher body mass, and shorter duration of diabetes predict better response to medications that target the GLP-1 pathway. 2. Simultaneous use of NSAIDs (for example ibuprofen) was associated with better responsiveness in women under 40. 3. Combining GLP-1 pathway medications with some other commonly used T2D medications (for example thiazolidinediones or sulfonylureas) may not have an additional benefit.