Diabetes, obesity & metabolism · 2026
Identifying Predictors of Early Treatment Intensification in Individuals With Type 2 Diabetes Treated With GLP-1 Receptor Agonists: A Machine Learning-Based Analysis of a Large Real World Cohort.
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Falcetta Pierpaolo, Zilich Rita, Baccetti Fabio, Baronti Walter, Morviducci Lelio, Musacchio Nicoletta, Muselli Marco, Ozzello Alessandro, Pisani Federico, Rossi Antonio, Salomone Enrica, Verda Damiano, Candido Riccardo, Ponzani Paola
Аннотация
AIMS: Despite the proven efficacy of GLP-1 receptor agonists (GLP-1 RAs), many patients with type 2 diabetes (T2DM) are not able to achieve glycaemic targets with these agents and they require additional therapies. Timely identification of individuals at higher risk of early intensification may improve outcomes and reduce therapeutic inertia.
MATERIALS AND METHODS: In this retrospective cohort study, we analysed data from 69 194 individuals with T2DM initiating GLP-1 RA. We applied logic learning machine (LLM), an explainable machine-learning algorithm, to identify-at the time of GLP-1 RA prescription-predictors of early intensification (≤ 12 months from GLP-1 RA initiation). For this purpose, we developed two distinct models: Model 1, which compared characteristics of individuals intensified early (≤ 12 months) versus those not intensified in the first year, and Model 2, comparing individuals intensified early vs. those never intensified (> 5 years of GLP-1 RA treatment).
RESULTS: Both models identified the same clinical phenotype, characterised by longer diabetes duration, unstable glycaemic control, prior insulin use, and cardio-renal complications. Model 1 showed limited discriminative ability (AUC 0.65) with many false positives, suggesting therapeutic inertia in real-world practice. Model 2, while selecting same predictors of Model 1, achieved better performance (AUC 0.78), suggesting clearer differentiation between patient cohorts.
CONCLUSIONS: Explainable AI identified a reproducible phenotype of patients associated with early intensification after GLP-1 RA initiation. These models may support earlier, more personalised treatment decisions in routine diabetes care.
MATERIALS AND METHODS: In this retrospective cohort study, we analysed data from 69 194 individuals with T2DM initiating GLP-1 RA. We applied logic learning machine (LLM), an explainable machine-learning algorithm, to identify-at the time of GLP-1 RA prescription-predictors of early intensification (≤ 12 months from GLP-1 RA initiation). For this purpose, we developed two distinct models: Model 1, which compared characteristics of individuals intensified early (≤ 12 months) versus those not intensified in the first year, and Model 2, comparing individuals intensified early vs. those never intensified (> 5 years of GLP-1 RA treatment).
RESULTS: Both models identified the same clinical phenotype, characterised by longer diabetes duration, unstable glycaemic control, prior insulin use, and cardio-renal complications. Model 1 showed limited discriminative ability (AUC 0.65) with many false positives, suggesting therapeutic inertia in real-world practice. Model 2, while selecting same predictors of Model 1, achieved better performance (AUC 0.78), suggesting clearer differentiation between patient cohorts.
CONCLUSIONS: Explainable AI identified a reproducible phenotype of patients associated with early intensification after GLP-1 RA initiation. These models may support earlier, more personalised treatment decisions in routine diabetes care.