ArticleDiabetes, obesity & metabolism2025
Evaluating prediction of short-term tolerability of five type 2 diabetes drug classes using routine clinical features: UK population-based study.
Article in Diabetes, obesity & metabolism, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Rates of, Reasons for, and Reactions to Discontinuation of GLP-1 Receptor Agonists: A Narrative Review.Diabetes, obesity & metabolism · 2026Review
- Trends in Second-Line Initiations and Treatment Outcomes Across Age and Frailty Groups in People With Type 2 Diabetes: UK Population-Based Study, 2019-2024.Diabetes, obesity & metabolism · 2026Article
- Predictors of Glycemic Response to Sulfonylurea Therapy in Type 2 Diabetes Over 12 Months: Comparative Analysis of Linear Regression and Machine Learning Models.JMIR diabetes · 2026Article
- One-year usage patterns of SGLT-2 inhibitors and GLP-1 receptor agonists in individuals with type 2 diabetes in a real-world population.Diabetes, obesity & metabolism · 2026Observational
- GLP-1RA precision medicine in people with type 2 diabetes: current insights and future prospects.The Journal of clinical investigation · 2026Review
- Evaluating prediction of short-term tolerability of five type 2 diabetes drug classes using routine clinical features: UK population-based study.Diabetes, obesity & metabolism · 2025Article
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Authors and funding
10 authors.
Funding
Abstract
aimsA precision medicine approach in type 2 diabetes (T2D) needs to consider potential treatment risks alongside established benefits for glycaemic and cardiometabolic outcomes. Considering five major T2D drug classes, we aimed to describe variation in short-term discontinuation (a proxy of overall tolerability) by drug and patient routine clinical features and determine whether combining features in a model to predict drug class-specific tolerability has clinical utility. MATERIALS AND
methodsUK routine clinical data (Clinical Practice Research Datalink, 2014-2020) of people with T2D initiating glucagon-like peptide-1 receptor agonists (GLP-1RA), dipeptidyl peptidase-4 inhibitors (DPP4i), sodium-glucose co-transporter-2 inhibitors (SGLT2i), thiazolidinediones (TZD) and sulfonylureas (SU) in primary care were studied. We first described the proportions of short-term (3-month) discontinuation by drug class across subgroups stratified by routine clinical features. We then assessed the performance of combining features to predict discontinuation by drug class using a flexible machine learning algorithm (a Bayesian Additive Regression Tree).
resultsAmongst 182 194 treatment initiations, discontinuation varied modestly by clinical features. Higher discontinuation on SGLT2i and GLP-1RA was seen for older patients and those with longer diabetes duration. For most other features, discontinuation differences were similar by drug class, with higher discontinuation for patients who had previously discontinued metformin, females and people of South-Asian and Black ethnicities. Lower discontinuation was seen for patients currently taking statins and blood pressure medication. The model combining all sociodemographic and clinical features had a low ability to predict discontinuation (AUC = 0.61).
conclusionsA model-based approach to predict drug-specific discontinuation for individual patients with T2D has low clinical utility. Instead of likely tolerability, prescribing decisions in T2D should focus on drug-specific side-effect risks and differences in the glycaemic and cardiometabolic benefits of available medication classes.
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