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Identifying Predictors of Early Treatment Intensification in Individuals With Type 2 Diabetes Treated With GLP-1

Pierpaolo Falcetta1, Rita Zilich2, Fabio Baccetti3

  • 1Diabetes Unit, Azienda USL Toscana Centro, Empoli, Italy.

Diabetes, Obesity & Metabolism
|March 10, 2026
PubMed
Abstract

Insights

Identifying patients needing earlier type 2 diabetes treatment intensification is key. Explainable AI identified a patient profile linked to early intensification after starting GLP-1 receptor agonists, aiding personalized care.

Area of Science:

  • Endocrinology
  • Artificial Intelligence in Healthcare
  • Pharmacotherapy

Background:

  • Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are effective for type 2 diabetes (T2DM), but many patients require additional therapies to reach glycaemic targets.
  • Early identification of patients at risk for treatment intensification can help overcome therapeutic inertia and improve outcomes.

Purpose of the Study:

  • To identify predictors of early treatment intensification in patients with T2DM initiating GLP-1 RAs using explainable machine learning.
  • To develop models that can support personalized treatment decisions in T2DM management.

Main Methods:

  • Retrospective cohort study of 69,194 individuals with T2DM initiating GLP-1 RA.
  • Application of logic learning machine (LLM), an explainable AI algorithm, to identify predictors of early intensification (≤12 months).
  • Two models were developed: early intensification vs. no intensification in year 1 (Model 1), and early intensification vs. never intensified (Model 2).

Main Results:

  • Both models identified a consistent clinical phenotype associated with early intensification: longer diabetes duration, unstable glycaemic control, prior insulin use, and cardio-renal complications.
  • Model 1 demonstrated limited discriminative ability (AUC 0.65), indicating real-world therapeutic inertia.
  • Model 2 showed improved performance (AUC 0.78), offering clearer differentiation between patient cohorts.

Conclusions:

  • Explainable AI successfully identified a reproducible patient phenotype associated with early intensification after GLP-1 RA initiation.
  • These AI-driven models have the potential to facilitate earlier and more personalized treatment decisions in routine diabetes care.
Keywords:
diabetes mellitus type 2glucagon‐like peptide‐1 receptor agonistsinsulinmachine learningtherapeutic inertiatherapy intensification

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